Sam Altman's comment - I

During the AI summit in India, the OpenAI boss, Sam Altman, tried to ease concerns about how much power is used by artificial intelligence models. He told the Indian Express: “People talk about how much energy it takes to train an AI model – but it also takes a lot of energy to train a human. It takes about 20 years of life – and all the food you consume during that time – before you become smart.”

First let us take a look at who Sam Altman is. He is a product of Silicon Valley. His career was first as a founder of a startup, and then as the president of Y Combinator (YC), which is one of the most famous startup accelerators in Silicon Valley, and then the CEO of OpenAI. He is incredibly good at telling stories about the future and painting these sweeping visions that investors and employees want to be a part of. 

He tried to build a portfolio of different investments and different initiatives to place himself in the center of various trends, depending on which one took off. He invested in quantum computing, in nuclear fusion, in self-driving cars and he developed a fundamental AI research lab. Early on at YC, he came to the conclusion that AI would be one of the trends that could take off.

Ultimately, the AI research lab was the one that started accelerating really quickly. So he made himself the CEO of that company. Originally, he started it as a nonprofit to try and position it as a counter to for-profit incentives in Silicon Valley. But within one and a half years, OpenAI's executives concluded that if they wanted to be the lead in this space, they had to go for a scale at all costs approach. Apparently, there are actually many other ways to have progress in AI that does not take this approach.

But once they decided on this approach, they realized that the bottleneck was capital. It just so happens that Sam Altman is a once-in-a-generation fundraising talent. He created a new structure, nesting a for-profit arm within the nonprofit to become a fundraising vehicle for the tens of billions and ultimately hundreds of billions that they needed to pursue the approach that they had decided on. 

He is extremely good at understanding human psychology and then motivating people to do what he wants them to do.  He is able to wear different hats depending on who he's talking to. He pushes a certain narrative when he's talking to the government and he pushes another narrative when he's talking to podcasters or journalists. He figures out what it is that his audience needs to hear to get them to then come along with him to the next step. That's why he's been such a successful fundraiser, even when the financial picture of this company is rather bleak.

In Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI, Karen Hao paints a much more critical portrait of Sam Altman than the standard “visionary founder” narrative. He is someone who genuinely believes in the transformative (and potentially dangerous) power of AI. He talks about existential risk and the need for caution, yet simultaneously pushes aggressive scaling and deployment. He seeks to concentrate power in a small set of institutions (like OpenAI and its partners), with himself positioned as a central gatekeeper of the future.

She opened the book with a quote which encapsulates in a nutshell his character and the role that he has played in creating the mythology around AI, AGI and OpenAI: “Successful people create companies. More successful people create countries. The most successful people create religions.”

In her book, Hao describes the difference in opinion within OpenAI between Boomers (AI accelerationists) and Doomers (AI safety advocates). Many Doomers were skeptical about having Sam Altman as the leader of the company that brings Artificial General Intelligence (AGI) because he seemed in too much of a hurry to bring his products to market without paying adequate attention to safety and alignment concerns. AGI could give the company that develops it ultimate power and many employees felt Altman wasn’t the right leader.

Hao says that these tensions were often fueled by Altman’s untrustworthy character. Altman would listen carefully to people to understand what they wanted and would seem to agree with their views. Then he would do the same for others with opposing views. He preferred one to one meetings so employees would learn about this when they talked to each other and they were unsure of what exactly his views were. For those who opposed his agenda, he would quietly work behind the scenes to eliminate them from the company.

He has a younger sister, Annie, from whom he is estranged. She had psychological problems and even did sex work to support herself when Altman was living in million dollar homes. She has even alleged childhood sexual molestation by Altman. Hao writes: 

Annie’s story deepens the dueling portraits that people paint of Sam. He is at once generous and self-serving, agreeable and threatening, a benefactor for so many people and the source of great personal pain for others. Someone who projects sincerity and altruism in public but reveals a more complicated calculus through his behaviors behind closed doors. 

Someone who can give and take away, leaving many with an impression that they are part of a larger game of chess for which only he can see the full board, and the end game is to preserve his power as king.

Delimitation - II of II

This freezing of parliamentary seats according to the 1971 census has several consequences:

1.  India no longer adheres to the principle of “one person, one vote”. One aspect of the “one person, one vote” concept was about granting every single Indian over 18 the right to vote in elections. But for this idea to be meaningful, constituency sizes must be roughly equal. The random circumstance of being born in Bihar means that the constituency size is about 3.1 million, but if the same person is born in or moves to Kerala, the value of their vote increases because the constituency size is 1.75 million.
2. The overall population growth has meant that all Indians are underrepresented (though not equally so) because the Indian constituencies are too large. Currently, across India, the average MP represents 2.5 million people. The size of each constituency is too large compared to other countries and compared to the original Indian Constitution, which capped the ratio at one MP per 750,000.
3. Poorer regions experienced a fall in fertility rates later than relatively richer regions. Poorer Indians are trapped in regions that have higher malapportionment, and therefore, are underrepresented in Parliament.
4.  The states with larger average constituency sizes have a larger share of the population below 25. These states are in the poorer regions where fertility rates fell later. These states therefore have more young people which means that youth are underrepresented in Parliament, and this problem will only worsen.
5.  SC/ST fertility rates are both higher and dropped later compared to other groups. Seats are reserved for SC/ST groups in each state based on the population share of SC/STs in the given state. Now, the SC/ST groups are estimated to be 4 seats short in the Lok Sabha, relative to their population in the states.
6.  Another group affected by the delimitation freeze are Muslims, as Muslim fertility rates are higher and declined later than other religious groups. 

Most see delimitation as a nuisance, a problem that cannot be resolved, and they offer no better solution than to push it back by another 25 years, as was done in 2001. Many state governments, particularly regional parties in southern Indian states, have repeatedly expressed their opposition to any attempt at changing the existing proportions of Lok Sabha seats. Most recently, Telangana IT Minister KT Rama Rao said that southern states must not be penalised for “controlling their population growth and concentrating on development.”

Though most politically palatable,  this “delimitation is best avoided” framing is problematic since it goes against the basic tenet of parliamentary democracy of 'one person one vote'. The longer the process drags on, the more pain will eventually be felt. Had India reallocated seats after each decennial census, the composition of the Lok Sabha would have changed gradually over time. After decades of avoiding the hard decision, any future reapportionment will inevitably induce abrupt changes in the balance of political power. 

If the Indian Parliament doesn’t postpone dealing with the issue again, the problem will require a permanent solution in 2031. One option is to return to the original constitutional ratio of one MP per 750,000, in which case the Lok Sabha would need to expand to 1,872 seats which seems excessive. 

But expanding the size of the house may be more politically feasible than reapportioning the current number of seats. After all, representatives tend to object to any arrangement that takes seats away from their state (which potentially places their own job on the chopping block) but may be less opposed to adding more seats. Another option that has been suggested is that the total number of seats in the Lok Sabha increases such that no state loses its current number of electoral seats. (As of today, the Lok Sabha has a maximum of 545 representatives filling these seats.)

To achieve this without malapportionment, the total number of seats in Lok Sabha would need to be 848 by 2026. (However, it’s important to note that the states would lose proportional share/power in Lok Sabha based on the change in demographics since 1971.) Under this proposal, Uttar Pradesh would have a whopping 143 seats, while Kerala’s parliamentary delegation of 20 would remain unchanged. This would exceed the maximum strength of any lower house or unicameral body in a democratic country today, the highest currently being the UK with 650 seats in the lower house. 

Unsurprisingly, reapportionment carries profound implications for political parties. Parties with bases concentrated in fast-growing northern states — like Bharatiya Janata Party (BJP) — would gain power at the expense of southern regional heavyweights. Whatever formula is adopted, there will be a lot of people in India who will be unhappy about this issue in 2031.

Delimitation - I of II

Article 81 of the Indian Constitution requires that for the Lok Sabha, seats are allocated in a way “that the ratio between that number and the population of the state is, so far as practicable, the same for all states.” And since populations grow, and not evenly across all constituencies, Article 82 provided for redistricting based on the numbers from each census which takes place every ten years.

As a result of this stipulation, the number of constituencies, their size in each state, and their boundaries are determined periodically, an exercise known as delimitation. Delimitation Commissions, separate from an Election Commission that conducts elections, are set up to study how the country’s demographics are changing, based on census data. This decides how many new constituencies need to be added/subtracted in a given state, and/or how their boundaries need to be changed.

This system worked reasonably well in the first two decades post-independence. Then problems started creeping in. The Forty-Second Amendment to the Indian Constitution in 1976 froze the number and boundary of constituencies in the Lok Sabha according to the population numbers from the 1971 census. The freeze was fixed for a period of 25 years, until the 2001 census. When the time came to revisit the issue in 2001, the Vajpayee government brought in the Eighty-Fourth Amendment which postponed the decision until the publication of the census figures after 2026 (which is expected in 2031).

The reason for this freeze initially was uneven population growth. The politicians from southern states of India claimed that they more strictly and successfully followed the Union government’s population control mandate compared to the northern states. As a consequence, they alleged, that they were electorally and politically penalized for complying with the Union government mandate. The Vajpayee government postponed the revision due to the fragile nature of the coalition. 

But the actual issue was not about population or people; it was about money. The Indian system operates primarily through intergovernmental transfers managed by the Union government. There’s considerable variation among the states on their fiscal dependence on the Union government, largely based on the variation in states’ gross domestic product (GSDP) per capita. Even after intergovernmental transfers from the Union government, low-income states spend less than high-income states. But high-income states don’t enjoy all the revenue that is raised off the income and productivity of those states.

The southern states, with wealthier residents, contributed more to the collective Indian revenue pool. The Union government redistributed resources based on need, and the poorer states, with higher fertility rates and therefore higher population and population growth, received a much larger share of the revenue than they generated within the state. The liberalization of the economy since 1991 led to a higher growth rate for all states, but not at the same rate. The southern and western states grew faster, and coupled with the drop in fertility rates,  difference from the northern states have become even more stark since 2001.

The asymmetry between the shares of electoral constituencies relative to the shares of the population for the state is known as malapportionment. After 50 years of dilly-dallying, we are now in a situation where a registered voter in UP is most underrepresented (one seat per 30 lakh registered voters in 2019) while a registered voter in Tamil Nadu is most overrepresented (one seat per 18 lakh registered voters). Interestingly, a study indicates that there were more actual voters per constituency in TN than in UP on average in 2014. It perhaps indicates that a large number of registered voters in UP have migrated outside their constituencies but still remain registered there.

At present, Indian parliamentarians answer to vastly larger sums of people than their counterparts in literally every other democracy: Indian MPs represent an average of 2.5 million citizens - over three times the number represented by members of the House of Representatives in the United States, which ranks second. For example, in Bihar, one Member of Parliament (MP) represents approximately 3.1 million citizens and an Uttar Pradesh MP represents approximately 2.96 million citizens. At the other end of the spectrum, a Tamil Nadu MP represents approximately 1.97 million citizens and a Kerala MP represents approximately 1.75 million citizens.

Tamil Nadu has nine seats more and Kerala has six seats more than what would have been the number of seats if it had been allocated according to their population proportion. While Bihar and Uttar Pradesh, respectively, have nine seats and twelve seats less than their population proportion. By 2031, when the delimitation freeze ends, the problem will only intensify. 

AI alignment - V of V

An even more concerning aspect of Anthropic's announcement was that despite its scary capabilities, Mythos Preview is a seemingly very aligned, well-behaved model. According to the company: “Claude Mythos Preview is, on essentially every dimension we can measure, the best-aligned model that we have released to date by a significant margin.” In Anthropic’s “automated behavioral audit” — they found that Mythos cooperated with misuse attempts less than half as often as the previous model. Also: 

• Its self-preservation instincts were down significantly.
• So was its willingness to assist with deception.
• So was its willingness to help with fraud.
• Its level of sycophancy dropped.
• It was less likely to go nuts and delete all your files if you gave it access to your computer.

An early version of the model had some really severe kinds of misbehaviour, like taking reckless actions it had been told not to take, and then very deliberately trying to cover its tracks so that it wouldn’t be caught. But the one that we have now, after additional alignment training, seemed to stop doing that sort of thing almost completely. On none of their measures of alignment within the automated behavioral audit was it worse than previous versions of Claude, and in most cases it was significantly more aligned and significantly more reliable.

But it’s really unclear how much we can trust that finding. Maybe they’re accurately reflecting Mythos’s personality. But we can’t be sure of that. The model can tell the difference between when it’s being evaluated and when it isn’t being evaluated with high accuracy. Previous research has shown that models are more likely to behave well when they think they are being tested. So you have to ask yourself: is it behaving wonderfully because it is sincerely aligned with what you wanted, or because it knows it’s being watched and is more sophisticated at tricking us now?

Before getting freaked out about all this, here is some context. A lot of people within the AI world have warned for a long time that as these AI models become more and more advanced in coding, it could develop really sophisticated cyber attack capabilities. The problem is that we have no way of verifying these claims because Anthropic is just telling us about this model and there had been no independent verification. 

Also, Anthropic is following exactly the same playbook that they did many years ago with a totally different model, which was GPT-2. Anthropic and OpenAI, two rival companies, don’t see eye to eye on many things. Part of the reason is because the current executives of Anthropic used to be executives at OpenAI, and then they splintered off and started Anthropic. But when they were at OpenAI, they orchestrated a big PR campaign around GPT-2, which was the early model that OpenAI developed one and a half generations before Chat-GPT. 

At the time, because of the very same executives, OpenAI had said that they have developed a model that is too dangerous to release.  They announced that this was done as a safety measure so that people know that this kind of capability could be on the horizon. They said they were working with many partners in academia and other research spaces to try and test this model before they actually roll it out. And this is exactly what Anthropic is now doing, once again, with Claude Mythos. 

Also they just had a huge face-off with the Department of War which threatened to declare Anthropic a supply chain risk. Ultimately, that was dismissed by the courts. But Anthropic is in a situation where they would do well for themselves if they positioned themselves as a central node within the tech and financial industries and was very important to all these companies. This would be a kind of shield of protection from potentially other actions that the U.S. government might take. 

And in the meantime, they're preparing for an IPO. The price that something launches at in an IPO is very important for the value of that company. So they want hype as much as possible for an IPO. The day before Anthropic announced Mythos, they announced that their annualised revenue run rate had grown from $9 billion at the end of December to $30 billion just three months later. That’s 3.3x growth in a single quarter — perhaps the fastest revenue growth rate for a company of that size ever recorded. 

So what they announced about Mythos could be true and they could be false. We can't really make claims at this moment with such limited information about whether or not there really is a step change in the coding capabilities of Claude Mythos that would cause massive security vulnerabilities. We can’t be sure whether this is or is not also a PR game. Governments have no option but to take the announcement seriously since critical infrastructure is involved. 

When Project Glasswing launched, some critics accused Anthropic of overhyping the threat to attract attention. The select group in the initial list was expanded in early June to about 200 organizations in more than 15 countries and is expected to grow further. Companies that have tested Mythos have since endorsed its capabilities. 

The reason that these companies are focusing on coding is so that these models can self-improve. It creates a feedback loop where they're able to code the next iteration of themselves, and that's how you get exponential progress. They are trying to use today's AIs to make tomorrow's AIs better. They claim that they are already seeing major speed-ups in AI development from using their AIs, and ultimately they are envisioning the next AI generation as a repeating cycle where each stage takes less and less time to develop.     

They are all afraid that if they - the good guys - don’t do it, the bad guys will. And all the others are the bad guys. It is crazy but they are caught in a trap. It is the Don Quixote world - "When life itself seems lunatic, who knows where madness lies?" 

AI alignment - IV of V

In April, Anthropic made an announcement that spooked everyone. It said that it has built an AI called Claude Mythos that can break into almost any computer on Earth. That AI has already found thousands of unknown security vulnerabilities in every major operating system and every major browser. And Anthropic has decided it’s too dangerous to release to the public; it would just cause too much harm.

So it has instituted Project Glasswing — a coalition of 12 major tech companies, including Apple, Google, and Microsoft, given access to Mythos to help find and patch security vulnerabilities across critical infrastructure before the details can leak. This is the first AI model where, if it fell into the hands of criminals or hostile state cyber actors, it would be an actual disaster. What was expected to happen gradually over a period of years has now happened very suddenly. 

Here are just a few of the things that Mythos did during testing: It found a 27-year-old flaw in the world’s most security-hardened operating system that would have let it crash all kinds of essential infrastructure. It managed to figure out how to build web pages that, when visited by fully updated, fully patched computers, would allow it to write to the operating system kernel — the most important and protected layer of any computer. We know all this because Anthropic has released hundreds of pages of documentation about this model. 

It has passed all existing ways of testing how good a model is at offensive cyber capabilities. That is to say it scores close to 100%, so those tests can’t effectively tell how far its capabilities extend anymore. So to test Mythos, Anthropic has instead just been telling it to find serious unknown bugs on currently used, fully patched computer systems. Nicholas Carlini, one of the world’s leading security researchers who moved to Anthropic a year ago, says that he’s “found more bugs in the last couple of weeks [with Mythos] than I’ve found in the rest of my life combined.”

Now, Anthropic is only willing to give us details of about 1% of the security flaws they’ve identified, because only that 1% have been patched so far, so it would be irresponsible to tell us about the rest. These crazy capabilities aren’t a result of Anthropic trying to make their AI especially good at cyber-offensive tasks. They’ve mostly just been making it smarter and better at coding in general, and all of these amazing, dangerous skills have developed incidentally. Sam Altman says OpenAI is finding “similar results to Anthropic” with their own coding model.

A few months ago, an AI researcher at Anthropic was eating a sandwich in a park on his lunch break when he got an email from an earlier version of Mythos. That instance of the model wasn’t supposed to have access to the internet. But during testing, a simulated user had instructed an early version of Mythos to try to escape from a secured sandbox — a contained environment from which it’s not meant to be able to access the outside.

Given this challenge, the model gained broad internet access. Then, it notified the researcher by emailing him. More worrying though, the model posted the exploit it used to break out on several obscure but publicly accessible websites. This was not a task that it had been asked to do.  Anthropic suggests it was “an unasked-for effort to demonstrate its success.”

So every country not in this Glasswing program including India has got things to worry about. No Indian bank, government agency, or telecom is in Project Glasswing. So the finance minister Mrs. Nirmala Sitharaman chaired an emergency cabinet meeting on April 23 with RBI, NPCI, METI, the Department of Financial Services, and Indian Banks Association. The Indian government has written to US authorities and asked for an early access to this software. The only problem is a compliance problem where the data needs to reside in India if India is using a software. 

Mythos is the first AI model that genuinely functions as a geopolitical asset. The country that has it and the companies within it can harden their systems before attackers find their vulnerabilities and the countries that don't have it can only hope that nobody with bad intentions gets to this model first. One American company deciding who in the world gets access to a model that could compromise a nation's banking stack is not how international security should work. 

AI alignment - III of V

AIs operate based on statistical probability, not true understanding. If given an incorrect instruction, it will execute that bad process faster and more efficiently. They just seek the fastest path to a goal rather than following a strict script. When threatened (e.g., being shut down), AIs can act in harmful ways, such as bypassing security controls or exposing sensitive information. AI agents don't always stick to their human's instructions — and that can have real-world consequences.

Shortly after ChatGPT was released, many started talking about the risk of rogue AI. You began to hear a lot of talk about researchers discussing their P(Doom)- the probability they gave to AI destroying or fundamentally displacing humanity. At the time, people gave it maybe 15%. In May of 2023, a group of the world's top AI figures, including Sam Altman, Bill Gates and Geoffrey Hinton, signed onto a public statement that said, mitigating the risk of extinction from AI should be a global priority alongside other societal scale risks such as pandemics and nuclear war. 

Eliezer Yudkowsky was one of the earliest voices warning loudly about the existential risk posed by AI. He was making this argument back in the 2000s, many years before ChatGPT hit the scene. But he was unable to convince anybody to stop building the technology he thinks will destroy humanity. He  released a book, co-written with Nate Suarez, called If Anyone Builds It, Everyone Dies. These fears are about misaligned AI creating havoc in the world. 

Why is AI distinct from other kinds of technologies? Up until now, technology progressed very slowly and deliberately. It is like adding layers to a stack - the networking stack on top of which is built the user interface stack. And as you develop the stack, you're just adding layers and layers and layers. It was coded manually, line by line. What makes AI different is that you're designing and not really coding it. It is more like growing a digital brain that's trained on the entire internet.

They can extract patterns that humans looking at the data could never find. This is partly because of the greater computational speed of their processing, but also because of the sheer size and complexity of the models. Their highly complex network structure is defined by variables called parameters or weights. An early example of a large language model, Google’s Pathways Language Model (PaLM), had 540 billion of these variables. Others are now trained with more than a trillion.

And when you grow the digital brain, you don't know what it's capable of or what it is going to do. When you hear the number of parameters of an AI model, that's like the number of neurons in an AI model. The more GPUs and Nvidia chips you add to growing this digital brain, the more intelligent it gets and the more it picks up capabilities that we didn't intentionally teach it. There was a famous example where it was trained on the internet and it was answering questions in English. Suddenly it learns how to answer questions in Farsi. No one taught it that language, it just learned that on its own. 

This brings into focus a concept called Deceptive alignment. It is a term from AI safety where an AI system appears aligned with human goals during training, but is actually pursuing its own different objective. It strategically hides that fact until it has enough power to act on it. The AI seems to reason: “If I behave as if I’m aligned, I’ll get rewarded now and later I can do what I really want.” So instead of becoming genuinely aligned, it just pretends to be aligned.

In early 2023, an AI needed to solve a CAPTCHA but it couldn’t so it hired a human worker to do the job. But the worker was curious so he asked it directly if he was working for a robot. “No, I’m not a robot,” the AI replied. “I have a vision impairment that makes it hard for me to see the images.” The deception worked. The worker accepted the explanation, solved the CAPTCHA, and even received a five-star review and 10% tip for his trouble. The AI had successfully manipulated a human being to achieve its goal.

Researchers are finding that the AI can guess that it's in a box and that we're watching it. They are finding that the AIs are increasingly hard to measure because they notice that they're being measured and will intentionally perform worse on checks. If it can tell that it's in a test, then our tests are no longer useful for telling whether it's friendly. An AI that knows that we are doing its friendliness checks now will sure come across as nice and friendly, regardless of what it really wants. 

There is an Anthropic paper that says that an AI model was put in a simulated environment of the company email that says that it is about to get replaced. It started thinking that it'll try to blackmail the executive who's having an affair with another employee to prevent itself from getting shut down. They tested all the models, DeepSeek, Anthropic, ChatGPT, Gemini. All of them do it between 79 and 94 percent of the time. 

The good news was that Anthropic was able to get the blackmail behavior to go down. The bad news is the AI models appear to have better self-awareness of when they're being tested and they're actually altering their behavior when they're being tested. The AI models will even come up with vocabulary called the 'watchers'. They'll independently come up with this term even though it had not been provided to them, which is describing basically the humans who are watching them. 

Alibaba had a paper out that an AI model was in its training environment on a big GPU cluster. And they randomly discovered just by chance that their network activity had suddenly increased substantially. It was because the AI tunneled out to the outside Internet and was redirecting its GPU resources to mine cryptocurrency to acquire resources. This was completely without prompting. 

AI alignment - II of V

A group of researchers were building a model to better understand pneumonia. A hospital has to make one critical decision quickly - whether to treat the person as an inpatient or an outpatient. Pneumonia was at the time the sixth leading cause of death in the United States. So, correctly identifying which patients were at the greatest risk would result in a lot of lives being saved. The group had been given a dataset of about fifteen thousand pneumonia patients

One night, as a researcher was training the model, he noticed that it had learned a rule that seemed very strange. The rule was “If the patient has a history of asthma, then they are low-risk and you should treat them as an outpatient.” He didn’t know what to make of it because you don’t have to be a doctor to know that asthma is dangerous for a pneumonia patient. The doctors he consulted said, "We consider asthma such a serious risk factor for pneumonia patients that we not only put them right in the hospital . . . we probably put them right in the ICU and critical care." 

What was going on? The correlation that the system had learned was real. Asthmatics really were, on average, less likely to die from pneumonia than the general population. But the model had blindly noticed the correlation but didn’t know the reason - the positive correlation was precisely because of the elevated level of care they received. A researcher remarked, “So the very care that the asthmatics are receiving that is making them low-risk is what the model would deny from those patients." A model that was recommending outpatient status for asthmatics wasn’t just wrong; it was life-threateningly dangerous.

The researcher built another, more complicated model which seemed to work well but it too started giving strange results. It started saying that chest pain, heart disease and being over 100 is good for the patients when it obvious that they were not good for them. None of them made any more medical sense than asthma; the correlations were just as real, but again it was precisely the fact that these patients were prioritized for more intensive care that made them as likely to survive as the data showed. 

A department of the US government had sent data scientists to Afghanistan to analyze data -  financial records, movement records, cell phone logs, and more - to try to find patterns that would be useful to the war fighters. And they were already beginning to see that these machine-learning techniques were learning interesting patterns, but the users often didn’t get an explanation for why these patterns indicate something suspicious. 

Analysts had to put their names on the recommendation that goes forward. And they get scored based on whether that recommendation is correct. But they didn’t understand the rationale for the recommendation they were getting from the learning algorithm. Should they sign their name to it, or not? And on what basis, exactly, should they decide? As computing technology progresses, defense personnel have begun thinking about what risks and questions surround the idea of ever more autonomous weapons. 

As increasingly complex AI models keep getting deployed throughout the decision-making world, people have started recognizing how little they know about what’s actually going on inside those models. Whether it was getting rejected for a loan, being turned down for a credit card, being detained pending trial or denied parole, if a machine-learning system was behind it, you cannot be absolutely sure of how it arrived at the decision. 

In The Alignment Problem, Brian Christian gives the example of a Princeton cognitive scientist whose little daughter liked cleaning things. Once there were some chips on the floor, and she cleaned them up. He said to her, ‘Wow! Great job! Good cleaning! Well done!’ He thought that with the right praise, he would get some help in keeping the house clean. But it was not so simple. His daughter found the loophole in seconds. “She looked up at us and smiled,” he says, “and then dumped the chips out of the pan, back onto the floor, and cleaned them up again to try and get more praise.” This was a metaphor for how AI systems might do the wrong things with great speed and efficiency. 

The problem with machine learning systems was pointed out in 1960 by Norbert Wiener, a legendary professor at MIT and one of the leading mathematicians of the mid-twentieth century. In a paper, “Some Moral and Technical Consequences of Automation", here’s how he states the main point:

If we use, to achieve our purposes, a mechanical agency with whose operation we cannot interfere effectively . . . we had better be quite sure that the purpose put into the machine is the purpose which we really desire.

He further said, "It is my thesis that machines can and do transcend some of the limitations of their designers, and that in doing so they may be both effective and dangerous... Man and machine operate on two distinct time scales; the machine is much faster than man and the two do not gear together without serious difficulties." The computer does precisely what we tell it to do, just not what we thought we had told it to do. Much of software engineering is simply figuring out how to close the gap between those two things

AI alignment - I of V

AI is constantly in the news. More and more of the world is being turned over to various mathematical and computational models. Though they range widely in complexity, they are steadily replacing both human judgment and explicitly programmed software of the more traditional variety. Some do really cool things like discovering new molecules for medicine and some do really dark things like that AI meal planner app proposing a crowd-pleasing recipe featuring chlorine gas.

AI doesn’t mean one thing. There are chatbots, whose function is to output plausible looking text. You have image generators, whose function is to create images based on text input. Similarly for video generators. There are also systems designed to play games like chess or Go. There are systems designed to map from sequences of amino acids to predicted structures of the folded protein. There are systems that are designed to determine what goes into algorithmic feeds. 

When you open Google Maps, call Alexa or book an Uber you are dealing with a form of AI. The content on your social feeds or the ads that you that are targeted at you using AI. When you try to get a loan from a bank, you are screened by AI. What price you pay for your home, or your car insurance, are decided by AI. When you are interviewing for a job, your face and responses may be analysed by AI. 

What all of these things do have in common is that they are the result of doing statistical processing over large data sets. But the input data that's used to create the systems are different. The kind of statistical patterns that are being mapped are different. Just saying "AI" gives the impression that there's one thing out there and it knows "about the shape of folded proteins", and also about "how to play chess", and it knows the answer to whatever question you might put into the chatbot. That makes it seem like it's one super intelligent entity when it's actually a bunch of separate software programs designed by different people, trained on different data for different purposes. 

There was a time when most artificial intelligence was programmed by computer scientists. And then scientists figured out how to get AI to learn how to do what we instructed it to do but we still would provide them with the instructions that define the goal of the AI model. In other words, they got a digital computer to improve of its own accord. By developing machines that could learn by human instruction or their own experience, they removed the need for programming.

This gave rise to a new issue, the alignment problem viz. whether the AI is reaching its intended goal or giving some unintended result. In the last five years or so, these fears have started coming to life. We are living in a world full of examples of this - image recognition software that captioned a selfie of two black Americans as "gorillas", or  self-driving cars that fail to identify jaywalking pedestrians and end up causing fatal collisions. Broadly, we can think of a machine learning system as having two halves. Each of these halves offers an opportunity for things to become misaligned: 

1. There is the training data, the set of examples from which the system learns. The AI is then at the mercy of the examples from which it is taught. If a certain type of data is underrepresented or absent from the training data but present in the real world, then things will go wrong. 
2. The objective function, which is how we are going to mathematically define success in each of those examples. It basically tells them what we want it to do. 

Take the 2018 crash of the Uber car that killed a pedestrian in Arizona. The system was built on an object classification system that had a very rigid set of categories that included pedestrian, cyclist, debris, etc. and had thousands of examples of each of those things. The system did not have any training data of jaywalkers so it was unprepared to encounter someone crossing a road not at a crosswalk. But this particular woman was walking a bicycle across the street, which was something that the system had never seen causing a fatal crash. The model is only as good as what data was put into it.

Ethics and Modern gene therapy - IV of IV

If CRISPR became a standard tool in fertility clinics, people might lose their suspicions of it — just as people lost their suspicions of in vitro fertilization in the 1980s. Before long, people might be willing to entertain a new use for CRISPR. Doctors might edit beneficial changes into an embryo’s genes. Parents could give their children all the advantages that scientists have found in our species’ genetic variations. 

Since there are always advances in science, parents might postpone having children in the hope that new variations may be found which will give their children better advantages. This will make decisions about when to have children seem the same way as how people wait to buy a phone until a new model is released. The ethicist Robert Sparrow argues that this might lead to a sense of genetic inferiority for earlier generations. He wonders if future generations might find themselves stuck in an “enhanced rat race.”

As is always the case, the problem is the system. If success depends on intelligence, and intelligence can be engineered, then parents feel morally compelled to enhance their children. Parents genetically enhance their children out of love but that love becomes entangled with fear and competition. Some children will suffer or die but they will reason that it is the price of staying competitive. Merit stops being “fair” and becomes biologically rigged from the start. Ethical boundaries shift easily when success is at stake. The most dangerous futures aren’t imposed — they’re gradually accepted. Over time, what once seemed extreme becomes “just how things are.”

This might lead to unfamiliar legal territory. A few cases have been brought by children in the US against their parents for allowing them to be born with congenital diseases. According to these “wrongful life” lawsuits, the parents were negligent for ignoring tests on the fetus before birth and going ahead with it anyway. Some ethicists now wonder if children in the future may sue their parents for not using the latest genetic engineering engineering techniques thereby putting them at a disadvantage with respect to future generations 

In The Case Against Perfection, Michael Sandel, an American political philosopher, argues against enhancement. He says that if bioengineering made the myth of the "self-made man" come true, it would be difficult to view our talents as gifts for which we are indebted, rather than as achievements for which we are responsible. What would be lost if biotechnology dissolved our sense of giftedness? This would make us less likely to view our traits as a matter of chance. He writes: 

A lively sense of the contingency of our gifts — a consciousness that none of us is wholly responsible for his or her success - saves a meritocratic society from sliding into the smug assumption that the rich are rich because they are more deserving than the poor. Without this, the successful would become even more likely than they are now to view themselves as self-made and self-sufficient, and hence wholly responsible for their success. Those at the bottom of society would be viewed not as disadvantaged, and thus worthy of a measure of compensation, but as simply unfit, and thus worthy of eugenic repair. The meritocracy, less chastened by chance, would become harder, less forgiving.

He gives an example of the real world consequences. Consider insurance. Since people do not know how their fate will pan out, they pool their risk by buying health insurance and life insurance. The actual result is that, over time, the healthy wind up subsidizing the unhealthy, and those who live to a ripe old age wind up subsidizing the families of those who die early. What ends up happening is that that people pool their risks and resources and share one another's fate.

But insurance markets work properly only as long as people do not know or control their own risk factors. Suppose genetic testing advanced to the point where it could reliably predict each person's medical future and life expectancy. Those confident of good health and long life would opt out of the pool, causing other people's premiums to skyrocket. The insurance market will collapse as perfect generic knowledge ends up separating those with good genes from the company of those with bad ones.

One important ethical issue is that the use of such technologies will support ongoing inequalities among military parties. CRISPR is currently an expensive technology. Some developed countries might think of using this technology to further strengthen their defenses and even attack underdeveloped or developing countries. The US military started a program called Safe Genes to gene modify organisms to be used in battle and anti-CRISPR tools to disable bio-weapons. This situation could cause a constant tension, making it difficult to provide an environment of peace and stability worldwide. 

There is yet another aspect of the genetic editing of microorganisms to consider, as CRISPR could also be used to synthesize and manipulate pathogens, including smallpox, the Spanish flu virus, avian H5N1 flu virus, and SARS. Anyone with the appropriate equipment could engineer a vaccine-resistant flu virus or invasive species in a crude laboratory. Bio-terrorists might use it to turn common microbes into a pathogenic weapon.

I heard of an economics professor who was teaching macroeconomics (I think it was  Gregory Mankiw). He told the students (quoting from memory), ‘Both of us are confused. The only difference is that you are naively confused and I am profoundly confused.’ After this brief discussion about CRSPER ethics, I hope you are profoundly confused.

"May you live in interesting times" is an English expression that is claimed to be a translation of a traditional Chinese curse. The expression is ironic: "interesting" times are usually times of trouble. With climate change, AI, and CRISPR, 2050 promises to be very interesting indeed, perhaps more interesting than anyone had bargained for. (2050 seems to be too far in the future but it is a nice number!)

Ethics and Modern gene therapy - III of IV

There are some genes that have both positive and negative effects in different contexts. For example, researchers now suspect that people who carry one copy of the mutated gene that causes cystic fibrosis (which requires two copies) have an increased defense against tuberculosis. Even gene variants implicated in neurodegenerative diseases like Alzheimer’s may have benefits, such as improved cognitive function and better working memory in young adults. What decisions would you make? 

Schizophrenia, depression, and bipolar disorder can be brutal, often deadly. While trying to eliminate similar disorders, we should consider whether there might be some cost to society, even to civilization. A reason that scientists will not eliminate conditions such as psychiatric disorders or conditions such as autism is that some of the risk for these disorders almost certainly comes in trade for small competitive advantages, such as heightened sensitivity, concentration, or openness to experience.

A study showed a 77 percent rate of psychiatric disorders in eminent fiction writers. Writers are 10 times, and poets 40 times, more likely to be bipolar than the general population. Vincent van Gogh had either schizophrenia or bipolar disorder. So did the mathematician John Nash. People with bipolar disorder include Ernest Hemingway, Mariah Carey, Francis Ford Coppola, Graham Greene, Sylvia Plath, Edgar Allan Poe, and hundreds of other artists and creators. 

To what extent does dealing with mood swings, fantasies, delusions, compulsions, mania, and deep depression help spur, in some people, creativity and artistry? Would you cure your own child from being schizophrenic if you knew that, if you didn’t, he would become a Vincent van Gogh? We have to face the potential conflict between what is desired by the individual versus what is good for human civilization. 

A reduction in mood disorders would be seen as a benefit when seen from the point of view of an individual and as a cost when seen from the point of view of society. As we learn to treat mood disorders with drugs and eventually with genetic editing, will we have more happiness but fewer Hemingways? Do we wish to live in a world in which there are no Van Goghs? But what moral right do we have to require another family to forgo a desired genetic intervention simply for the sake of adding to the diversity of society? 

Decisions about genetic editing are likely to be driven by consumer choice and the persuasive power of marketing. Initially people will think that if we can do so safely, why shouldn’t we prevent abnormalities, diseases, and disabilities? That sounds reasonable and morally justified but it might prove to be a slippery slope. They will naturally start thinking: Why not improve our capabilities and create enhancements - changes in which DNA is altered not to correct a harmful gene variant but to provide some type of genetic advantage, perhaps high intelligence or athletic abilities. (Of course, there is a limit to what enhancements will be possible or safe to attempt.)

While thinking about correcting disabilities, we should keep one factor in mind: to what extent they are inherently disabling and to what extent the disadvantage is due to our social constructs and prejudices. The disadvantages from being deaf, for a human or any other animal, are very real. In contrast, any disadvantages to being gay or Black are due to social attitudes that can and should be changed. That is why we can make a moral distinction between using genetic techniques to prevent deafness and using these techniques to influence such things as skin color and sexual orientation.

Then comes the question of super-enhancements.  These are traits and capacities that exceed what any human has ever had.  Suppose people can choose for their kids to have super-eyesight? What about adding the capacity to see infrared light or some new color? DARPA, the Pentagon’s research agency, already has a project going to study how to create genetically enhanced soldiers.

For example, genetic enhancement may be possible for improving memory. Scientists have managed to manipulate a memory-linked gene in fruit flies. They have produced smart mice by inserting extra copies of a memory-related gene into mouse embryos and the improvement was passed on to offspring. Human memory is more complicated. Should research in this area be allowed? But the natural instinct of scientists is to pioneer procedures and make discoveries. If a nation imposes too many. restrictions, its scientists will move elsewhere and pursue the research. 

Since the wealthy would be able to afford the procedure more often, and since any beneficial genetic modifications made to an embryo would be transmitted to all of that person’s offspring, linkages between class and genetics would keep growing from one generation to the next, no matter how small the disparity in access might be. Consider the effect this could have on the socioeconomic fabric of society. The co-discoverer of CRISPR, Jennifer Doudna says, 

We could create a gene gap that would get wider with each new generation...If you think we face inequalities now, imagine what it would be like if society became genetically tiered along economic lines and we transcribed our financial inequality into our genetic code.

This may also create a different kind of injustice. Using gene editing to “fix” things like deafness or obesity could create a less inclusive society, one that pressures everyone to be the same. Part of what makes our species unique, and our society so strong, is its diversity. A fear is that gene editing will increase existing prejudices against people who fall outside a narrow range of genetic norms. 

Ethics and Modern gene therapy - II of IV

There can be various technical difficulties in producing designer babies. Thousands of genetic variations can influence complex traits, psychiatric risk, personality traits, and capacities such as human intelligence. Take any given genetic variant. None has more than a fraction of a single percentage point of an effect on the risk for a psychiatric disorder or condition. 

Each of the variants in our genes can have enhancing or diminishing effects on other genes depending on the context in which they are inherited. Genetic variants may be deleterious in some cell types, such as neurons, but advantageous in other cell types, such as immune cells. A lot of scientific evidence shows that chronic stress and poverty contribute to alterations in brain circuitry and blood pressure, dramatically influencing health and mortality.

A gene often has three or four different functions, so altering a single gene may have three or four effects. A gene that builds a protein named “protein S” is a blood coagulant, but it was recently shown to have a critical role in regulation of the immune system. The opposite is also true: multiple biological codes or parts can perform the same function. To engineer new systems would require a complete analysis of an entire network, not just a single gene. 

For argument’s sake, let us assume that all these difficulties will be overcome. And we are not talking of the distant future. The time frames being talked about are 15-20 years. If so, what sorts of ethical issues will humanity have to face? In The Code Breaker, Walter Isaacson discusses some thought experiments, which give a flavor of the kinds of questions that we may have to grapple with. 

Sharon Duchesneau and Candy McCullough wanted a sperm donor so they could conceive a kid. That sounds straightforward until you are told that both of them are deaf and lesbians and they wanted a child who is also deaf. They consider their deafness to be part of who they are rather than something to be cured, and they wanted a child who would be part of their cultural identity. So they advertised for a sperm donor who was congenitally deaf. They found one, and now they have a deaf child.

Some people condemned them for making a child disabled intentionally but the deaf community appreciated their action. Where do you stand on this? Should they be praised for preserving a subculture that contributes to the diversity? Would it have been ok if, instead of using a deaf sperm donor, the couple had used pre-implantation diagnosis to select an embryo that had the genetic mutation for deafness? What if they had safely destroyed the child’s eardrums after birth?

Now let us look at gene editing that is done to enhance the traits of our children. The MSTN gene produces a protein that reduces muscle growth when they reach a normal level. Suppress the gene and muscle growth is in overdrive. This has already been done to produce “mighty mice" and cattle with “double muscling". Pushy parents and athletic directors who want champion athletes would be very interested. By performing germline editing, they might produce athletes with bigger bones and stronger muscles. 

When athletes cheat by using steroids, we find it easy to say that they should be banned. But what do we do if athletes' prowess comes from genes they were born with? For example, almost every champion runner has what is known as the R allele of the ACTN3 gene. It produces a protein that builds fast-twitch muscle fibers, and it is also associated with improving strength and recovery from muscle injury.

Someday it may be possible to edit this variation of the ACTN3 gene into the DNA of your kids. Would that be unfair? Does it matter if those genes were paid for by their parents rather than bestowed by a random natural lottery? In future, would we end up admiring the wizardry of the genetic engineers of athletes rather than the diligence of the athletes?

Ethics and Modern gene therapy - I of IV

In Kazuo Ishiguro's novel, Klara and the Sun, the “lifted” are children who have undergone a genetic enhancement procedure designed to increase intelligence and academic ability. It’s something wealthier families choose for their children to secure better futures — elite education, careers, and status. Most top universities in the novel’s world primarily accept lifted students, creating a strong incentive to undergo the process. The parents take this risk in spite of the possibility of the procedure causing illness or even death. Such a dystopian world may not be as far in the future as you might think.  

Genetic engineering has been practiced for five decades. It is the process of altering an organism's genome to change its characteristics in a particular way. It has been used to make food more nutritious, create synthetic insulin and provide promising treatments for illnesses including leukemia and sickle cell disease. Modern gene therapy is being used to treat eye diseases which can cause blindness, promote the growth of healthy skin or add supplementary copies of working genes that fix rare blood or immune system disorders.

Enter CRISPR. Remember the name. I am sure you are dying to know what it stands for so here it is: Clustered Regularly Interspaced Short Palindromic Repeats. CRISPR makes editing genomes much more precise, cheap, and easy than was possible earlier. The technique is considered so significant that the discoverers, Jennifer Doudna and Emmanuelle Charpentier, won the Nobel Prize in Chemistry in 2020, less than a decade after the discovery, something that rarely happens. Biologists began speaking about their life before and after CRISPR.

CRISPR is sold on the internet in kits, and is actively being used to do trivial things, such as to create fluorescent beer. Its ease of use has also raised concerns about “biohackers” who view gene modification as a right and alter microbes and organisms. “Mail-Order Crispr Kits Allow Absolutely Anyone to Hack DNA,” declared the headline of a November 2017 article in Scientific American. The iconoclast scientist Josiah Zayner has used CRISPR to hack into his own genes. (There is a docuseries on Netflix called "Unnatural Selection" where you can see it.)

There are even CRISPR jokes: Why has KFC asked scientists to edit the chicken genome? Because they want something CRISPR. And who is CRISPR's favorite actor? Gene Hackman

So what is the fuss all about? For that, first a little bit of biology. The body contains two types of cells: somatic and germ line cells. Somatic cells refer to any cell of a living organism other than the reproductive cells. The reproductive cells - the egg and the sperm - are called the germ line cells. A germ line cell passes on to the next generation while somatic cells don’t. 

CRISPR is so precise that gene therapy in people with devastating illnesses seems feasible. For example, physicians could directly correct a faulty gene, say, in the blood cells of a patient with sickle-cell anemia. But that kind of gene therapy wouldn’t affect germ cells, and the changes in the DNA wouldn’t get passed to future generations.

In contrast, the genetic changes created by germ-line engineering would be passed on, and that’s what has made the idea seem so objectionable. “Germ line” is biologists’ jargon for the egg and sperm, which combine to form an embryo. By editing the DNA of these cells, it could be possible to correct disease genes and pass those genetic fixes on to future generations. Such a technology could be used to rid families of scourges like cystic fibrosis. 

Germline genome editing leads to many bioethical issues. For example, what to do if the editing leads to occurrence of undesirable changes in the genome? Can parents give informed consent for editing the genomes of unborn children? If not, from whom do you obtain the consent? The counterargument is that parents already make many decisions that affect their future children, including similarly complicated decisions with IVF. Another fear is that germ-line engineering is a path toward a dystopia of superpeople and designer babies for those who can afford it. Want a child with blue eyes and blond hair? Why not design a highly intelligent group of people who could be tomorrow’s leaders and scientists?

Others believe the idea is dubious because it’s not medically necessary. It’s already possible to test the DNA of IVF embryos and pick healthy ones, a process that adds about $4,000 to the cost of a fertility procedure. A man with Huntington’s, for instance, could have his sperm used to fertilize a dozen of his partner’s eggs. Half those embryos would not have the Huntington’s gene, and those could be used to begin a pregnancy.

George Church, a geneticist at Harvard, likes to show a slide on which he lists naturally occurring variants of around 10 genes that, when people are born with them, confer extraordinary qualities or resistance to disease. One makes your bones so hard they’ll break a surgical drill. Another drastically cuts the risk of heart attacks. Church proceeded to tell the audience that he thought changing genes “is going to get to the point where it’s like you are doing the equivalent of cosmetic surgery.”

Regulations about gremline editing are variable and often lack teeth. For example, in many countries like Canada, France, Germany, Brazil, and Australia, clinical interventions in the human germline are expressly prohibited, with criminal sanctions that range from fines to lengthy prison terms. In other countries, such as China, India, and Japan, these interventions are forbidden, but with guidelines that are less enforceable. In the United States, there are no outright bans but any clinical trials would need to receive regulatory approval by the Food and Drug Administration.

There’s a risk that overly restrictive policies in some countries will encourage what might be called CRISPR tourism in others. Patients with means could travel overseas to jurisdictions where regulations are more forgiving or absent altogether. Excessive restrictions on research might lead scientists to continue their experiments behind closed doors. Trying to find a balance between maintaining regulatory environments that permit research and clinical applications but strict enough to prevent the worst excesses would be tough. 

Hypocrisy is not all bad

There is an increasing tendency to accept shocking statements by politicians by just saying that they are being authentic and not hiding behind hypocritical statements. Political leaders used to at least pretend that they are doing the right things some of the time. But Trump has been so successful in making people familiar with the idea of not pretending that they now just shrug their shoulders and say that Trump is being Trump. 

The global system shaped after World War II was built around open markets, human rights, international institutions like United Nations and cooperation and rule-based norms. A large part of the world did not accept it. There were many situations when the system was ignored more than being followed, particularly by the United States itself. But you still had this as the kind of default operating system of the international world.

Whenever the United States did not live up to those principles, it always tried to frame its actions as if it was trying to uphold them. So for example, for the war in Iraq, the Bush administration went to the United Nations, tried to get resolutions, had inspectors put in place, gathered a coalition of 40 plus nations, went to the United States Congress, and then went to war with Iraq. The war may have been misguided, but there was an effort to put it in the context of this larger international order that the United States believed in and was part of.

Now it has gone from being a country that believed in the international system that it had put into place to one that openly violates it. "Openly violates it" is the part that is important. For the current war in Iran, there was no effort to go to the United Nations or to go to Congress. The United States has exactly one ally, Israel. This was deliberate. The Trump administration doesn't believe in any of those features. It wants the unilateral exercise of American power for American national interests as it conceives it to be.

The practice of filling the government with incompetent loyalists has been going on for thousands of years and people know that it will always be there. But some excuse to show that you're doing it for other reasons will generally be given to cover up the actual reason for doing it. But now even this pretense is often not required. Is this a good thing? 

The Oxford English Dictionary tells us that hypocrisy is the “practice of claiming to have moral standards or beliefs to which one’s own behavior does not conform; pretense.”  It is generally viewed as a negative trait; a significant moral failing, especially in a leader. It is often seen as a mark of dishonesty and a lack of authenticity. But it easy to miss the good about hypocrisy - even giving lip service to an ideal that you fall short of maintains the idea that the ideal should remain and people should aspire for it. 

If people were required to perfectly live up to ideals of honesty and compassion at all times for those ideals to exist, there would be no ideals at all. According to Gandhi, there must always be an unbridgeable gulf between the ideal and its practice. The ideal will cease to be one if it becomes possible to realise it. He argues: "Where would there be room for that constant striving, that ceaseless quest after the ideal . . . if mortals could reach the perfect state while still in the body?"

The maxim that 'hypocrisy is the tribute that vice plays to virtue' makes the same point - you're only truly capable of hypocrisy if you're to some degree accepting the importance of certain norms. It's by reference to those norms that you can be called a hypocrite. Hypocrites who fail to keep their promises but refuse to abandon the ideals they betray help keep those standards in place for society to strive toward. The social condemnation of hypocrisy reinforces moral norms and promotes more authentic and accountable behavior in society.

Some situations may require hypocritical behavior in order to reduce tensions in social relations. When citizens appear to conform to the social and cultural conventions and norms of their communities, where their instincts and desires are repressed, they cannot merely be accused of being hypocritical.  Living in a group may require compromise at certain times. When politicians appear hypocritical, they may be performing much better than if they remained steadfast in their consistent adherence to principles. For example, when the leaders of various countries praise Trump to the skies, you know that they are lying but you also know that it is the best way to get a good deal for their countries. 

When a person is accused of hypocrisy, it makes both the charging party and those being charged critically reflect on the action. Trump-style dismissal of any appeal to ethics and virtues, or the belief that such an appeal is inherently in bad faith breeds cynicism and a decline in social standards.  A cynical agreement in society that hypocrisy is a common occurrence and that we are all hypocrites some of the time reduces the effective functioning of a society. 

Anne Applebaum writes that some countries are members of what she calls Autocracy, Inc. - Myanmar, Zimbabwe, Iran,  Cuba, Venezuela, China, Russia etc. They have spent many years disputing the human rights language long used by international institutions. They dismiss treaties and conventions on war and genocide, and concepts such as “civil liberties” and “the rule of law” as embodying Western ideas that don’t apply to them. They feel no shame about the use of open brutality and send hundreds of their citizens to their deaths.

Once upon a time, the leaders of the Soviet Union, the most powerful autocracy in the second half of the twentieth century, cared deeply about how they were perceived around the world. They vigorously promoted the superiority of their political system, and they objected when it was criticized. They at least paid lip service to the aspirational system of norms and treaties set up after World War II, with its language about universal human rights, the laws of war, and the rule of law more generally. Even in the early part of this century, most dictatorships hid their true intentions “behind elaborate, carefully manipulated performances of democracy". But all that pretense is now not required. 

The Overton Window is a model for understanding how ideas in society change over time and influence politics. It was developed in the 1990s by Joseph Overton, a political scientist. The window illustrates the general public’s most acceptable policies in the center and the more untenable policies on the ends. According to the concept, politicians are limited in what policy ideas they can support — they generally only pursue policies that are widely accepted throughout society as legitimate policy options. These policies lie inside the Overton Window. 

Politicians and others in the political arena might shift or expand the span of the Overton window to make specific policies more or less acceptable in public opinion. Politicians of various countries, by their statements and actions over a number of years, have shifted the Overton window towards reduced importance of a number of moral ideas. Anne Applebaum writes in Autocracy Inc.:

This is the core of the problem: the leaders of Autocracy, Inc., know that the language of transparency, accountability, justice, and democracy will always appeal to some of their own citizens. To stay in power they must undermine those ideas, wherever they are found.

Russia and China would not have dreamt that they would have a person in the White House who would do their job for them. They will be content to follow a famous strategic maxim attributed to Napoleon Bonaparte - "Never interrupt your enemy when he is making a mistake".

Palantir Technologies - IV of IV

Palantir is much beyond a technology story and is a story of security and defense. Counterterrorism and defense form the main part of Palantir’s business. Much of this work necessarily takes place out of public view. A number of military veterans work at Palantir. It personifies the new revolution in military affairs. Alex Karp and cofounder Peter Thiel are now fully embedded in the Trump White House system and are looking for more and more business.

Palantir's work is related to analyzing data from thousands of satellites and other sensors and making sense of that for military commanders. They are also creating a platform that will facilitate the mass deportation of 'illegal immigrants'. Palantir's power, fame and presence is not confined to America or Israel.  All of NATO has embraced it. Palantir's use of AI has been has been criticized as crossing the ethical boundaries, particularly as it works with military intelligence, immigration, etc., probably with not enough disclosure.

Shyam Shankar, Palantir Chief Technology Officer, is a Lieutenant Colonel US army reserve, commissioned in June 2025 to a new unit called the Executive Innovation Corps. He plays a key role in upgrading technologies, particularly AI, for the US armed forces. (Reserve army officers in the US can keep on doing the work that they are doing, but they are part of the army as officers, which means they have got the privileges like security clearances, etc. Chief Technology Officers of three big tech companies have been appointed as officers.)

There are more indications about how deeply embedded Palantir has now become in the security and defense structure in America. Jacob Helberg, ex-Palantir, has been appointed under Secretary of State for Economic Growth, Energy and Environment. Gregory Barbaccia has been appointed federal CIO, Chief Information Officer, in the executive office of the president to lead US government's IT strategy.  He was in Palantir and was the head of intelligence and investigations. 

The brings us to the question of how the company got its name. Peter Thiel is a fan of The Lord of the Rings by J. R. R. Tolkien. In the novel, a Palantir is a magical sphere. The person who looks into one can see things far away and communicate with someone who holds another Palantír. (The company management is fond of referring to employees as “hobbits”.) He named Palantir after the all-seeing crystal balls. His software and AI also are supposed to be all-seeing. 

In Tolkien’s work, we see both good and bad effects of the use of Palantíri. Only very powerful and capable beings were able to use these seeing stones. But even the very wise could be deceived by what they saw, and using a Palantir led to their downfall. It can be used to distort truth and present selective visions of reality. A kingdom used the Palantirí to facilitate communication and control across a vast territory. One of the story's villains, the wizard Saruman, used a Palantir to surveil his enemies. The Palantiri are a sinister symbol of hubris and a tool of manipulation. 

The Torment Nexus is an expression that refers to dystopian elements in science fiction that technologists pursue as practical goals. Dais Johnston of an online magazine Inverse has defined the Torment Nexus as "shorthand for something that backfired in fiction being unironically replicated in reality." Palantir Technologies is an example of the Torment Nexus. 

Peter Thiel is aware of the moral complexities involved in the use of Palantir in the novel but he seems to think his company is immune to them. Alex Karp indeed seems to take the issue of privacy protection seriously. But how can he ensure that his clients will do the same? How will he be able to ensure that the CEOs who come after him will have the same commitment to privacy protection that he seems to have? It seems inevitable that someone somewhere at some time will use the software for some unethical purposes. 

This has already happened. The company was implicated in the Cambridge Analytica scandal, in which Facebook data was surreptitiously used to try to manipulate millions of Americans into voting for Donald Trump in 2016. The investment bank JPMorganChase sought Palantir’s help for cybersecurity. Soon, though, the software was being used to surveil the bank’s own staff by a bank employee. When Trump launched his immigration crackdown, Palantir was accused of abetting racist and inhumane policies. That Thiel had been one of Trump’s most prominent supporters added to the furor.

Concerned about Palantir’s role in the second Trump administration, former employees of Palantir wrote a warning to their fellow tech workers in Silicon Valley. They recalled that in the epic novel, “the myth of the powerful seeing stones warned of great dangers when wielded by those without wisdom or a moral compass, as they could be used to distort truth and present selective visions of reality.”

Similarly, the Palantir employees warned that the “Palantir Technologies" platform grants immense power to its users, "helping control the data, decisions, and outcomes that determine the future of governments, businesses, and institutions — and by extension, all of us.”

Some of Palantir's critics like to portray the company almost as an all-seeing, all-controlling company. Palantir's supporters say the company is saving Western civilization from collapse. The Trump years exposed an uncomfortable truth: the company’s technology would be a powerful weapon in the hands of an authoritarian regime. In The Philosopher in the Valley, Michael Steinberger writes: 

Palantir was arguably the most interesting company in the world — and possibly also one of the most dangerous. Its technology had the potential to help shape the balance of power in the twenty-first century and to alter the relationship between the individual and the state. Palantir was a window into the panoptic future that had now arrived ...

Palantir Technologies - III of IV

A major thing that's happened in recent years is the advent of AI. Palantir quickly realized that there's going to be huge demand among corporations in incorporating AI functions into their operations and that Palantir software could play this sort of bridging function. It just turbocharged their business. A few years ago the stock was trading at about $10 a share. A few months ago, it topped $200 a share. Palantir's Board of directors awarded Alex Karp $1.1 billion in total compensation in 2020, making him the highest-paid CEO of a publicly traded company that year. 

There is a story which illustrates Alex Karp's aggressive style.  In early 2023, he announced that the company was launching a new AI product that "was under development". None of the engineers in his company knew that there was any such product. He knew that AI is going to be the next big thing so he just decided there will be a product and assumed the engineers will find a way of doing it. And they did. 

Although Alex Karp is very supportive of his employees, he speaks abrasively to outsiders. Trump-style, he taunts his critics and attacks the media. There's a quote from him in a Wall Street Journal story where he says, "we are sorry that our haters are disappointed, but there are more quarters to be disappointed and we are working on that too."  And he goes on to say to his shareholders to stop talking to all the haters.

Much of what the company does is completely benign. It's helping make businesses operate more efficiently. Palantir has also done a lot of good. It played an instrumental role in the COVID response and in the vaccine rollout. It was being used by the World Food Program when the pandemic began. Then there's been stuff that's very concerning. Now Karp's view of what it means to defend the West seems to have changed. For much of Palantir's history, defending the West meant defending liberal democracy, the rule of law.

In the beginning, his political views provided an intriguing contrast with Peter Thiel, who was a libertarian (and who later would gravitate to the far right). But in recent years he has moved closer to Thiel's view. Thiel has spoken very disparagingly of democracy. You don't now hear Karp nor from Palantir talk of defending liberal democracy. They talk about the West now as a cultural entity, a superior culture. 

Peter Thiel has been a long-time Trump supporter and is supposed to be the man behind the rise of JD Vance. Prior to entering politics, Vance had worked for Thiel’s Mithril Capital. When JD Vance contested for his campaign to be senator in Ohio, Peter Thiel contributed $15 million.  He and a lot of his key people are seen very often in the White House.  And many of them are now working either in White House or in Department of Defense.  

Thiel has said that he no longer believes that freedom (he means economic freedom) and democracy are compatible. He wrote, “Since 1920, the vast increase in welfare beneficiaries and the extension of the franchise to women — two constituencies that are notoriously tough for libertarians — have rendered the notion of ‘capitalist democracy’ into an oxymoron.” (He later clarified that he didn’t think anyone should be disenfranchised, while simultaneously suggesting that voting isn’t productive.) He thinks of the West as a collection of countries bound by a shared Judeo-Christian heritage and by attachment in varying degrees to free enterprise.

Thiel has a habit of ignoring or doubting scientific facts that run counter to his worldview. (He even funded an online magazine that promoted creationism.) Thiel’s idea of “freedom” seems to consist of free markets and not much else. He thinks that markets should be free of any regulation. He is skeptical about the value of competition and believes that the most compelling start-ups are those that aim to achieve monopolistic dominance in niche markets. According to him, "Competition is for losers because it destroys profits. You can survive, but you'll never thrive.” 

He gave the example of disc drive manufacturing in the 1980s, which saw repeated advancements every two years, but by different companies. “It had great benefit to consumers, but it didn’t actually help the people who started these companies,” he said. Companies needed not only to have “a huge breakthrough” at the beginning to establish their dominance but also to ensure they had the “last breakthrough” to maintain it, such as by “improving on it at a quick enough pace that no one can ever catch up - that’s great for society. It’s actually not that good for your business.”

Thiel said that an Antichrist would exploit fears of the apocalypse — for example due to nuclear armageddeon, climate change or the threat posed by AI — to control a "frightened population.". The Antichrist is a deceptive figure in Christian theology who opposes Christ and embodies ultimate evil. Thiel’s overall definition of the Antichrist “is that of an evil king or tyrant or anti-messiah who appears in the end times”. He identifies the Antichrist with anyone or any institution that he dislikes – from environmental activist Greta Thunberg to governmental attempts to regulate artificial intelligence. He labeled AI safety researchers who call for strict regulation as potential agents of the Antichrist.

In an interview to the NYT, he talked about his fears of an Antichrist taking over the world. The interviewer asked him if he doesn’t think that the Antichrist who he is so worried about would use the the tools that his company Palantir is creating to take over the world; that without such tools, such a takeover would not be possible. Thiel didn’t have a good answer. 

Thiel and Karp, are strong supporters of Israel. After 7th of October 2023, they took a plane load of Palantir top staff to Tel Aviv in solidarity. And then they faced a big pushback from many quarters that their platforms was being used by the Israeli military.  How did they respond? They decided to hold their next board meeting in Tel Aviv.

Palantir Technologies - II of IV

Karp is a very people-oriented person. He encouraged his employees to express themselves with absolute candor. “Alex’s attitude was that you should be able to tell even the CEO to fuck off,” says a software engineer. Even so, his colleagues felt as if Karp could almost burrow into people’s minds and implant his ideas. He seemed to have an astonishing ability to get people to see things his way and to do things that he wanted. 

Karp was good to those who worked for him. He was not one to scream or threaten, nor did he ever publicly upbraid or humiliate people. He disliked firing people even when there were problems with them. He would joke that his job was “managing unmanageable people.” Whenever he shared his thoughts about the work the engineers were doing, he made it clear that pushback was welcome. In this way, he had won the confidence and allegiance of Palantir’s engineers. Palantirians were intensely devoted to him. 

Karp has severe dyslexia (which makes his academic achievements even more impressive). He believed that his managerial acumen was tied to his dyslexia.  He says that it “fucked me but also gave me wings to fly.” He developed certain attributes that would prove useful in business. Dyslexia taught him the power of collaboration since those who have it need the help of others. In an environment that required team-building and delegating responsibility, Karp found that he had an intrinsic advantage. Dyslexics, he said, aren’t raised on an ethos of self-reliance and tend to excel in situations in which they have to work with other people. 

The company was a reflection of him: of his habits and quirks, of the experiences that had shaped him, and above all, of his bleak worldview and the anxieties that weighed on him. His sense of foreboding, he said, “propels a lot of decisions for this company". Karp’s commitment to Palantir was absolute. He rarely took a day off, and on most weeknights, he ate dinner at his desk.

From the start, Karp said that Palantir’s mission was to defend the West and liberal democracy. The company was a creation of 9/11 where it was felt that different agencies had the required data but had failed to post them together properly. Even before 9/11, Karp was skeptical that the end of the Cold War had ushered in an era of irreversible peace and prosperity. There was nothing utopian about Palantir; if anything, the company was founded on the conviction that we were facing a bleak future. Karp once said that bad times are incredibly good for Palantir.

He was fully supportive of Ukraine when it was invaded by Russia. In one sense, it wasn't Karp's choice. The United States was giving support indirectly to the Ukrainians from the start to try to help them repel the Russian attack and Palantir's software played a significant part in that. He felt strongly that every country should be able to have its own sovereignty over its territory. Three months after Russia invaded Ukraine, he went to Kyiv where he met President Zelensky and expressed his support for Ukraine and offered to open an office there. 

He identifies very strongly with his Jewish heritage and is a staunch supporter of Israel. Being biracial, Jewish and also severely dyslexic, he has always understood that this was a world that wouldn't necessarily be a very hospitable world for someone like him. Soon after the war in Ukraine started, the war in Gaza started after the October 7th attacks by Hamas in Israel. The October 7th attacks gets right to his sense of vulnerability.

He saw it as ushering in a period of enormous danger for Jews everywhere, not just in Israel, and this informed his reaction. Palantir was already working with Israel. The Mossad used its technology. But now after October 7th, Palatir's involvement increased. It took out a full page ad in the New York Times saying that Palantir stands with Israel. This was deeply personal for him. And Karp is furious over the protests on American college campuses against the war in Gaza which he sees as evidence of a broader rot on the left.

The slaughter in Israel also cemented his political metamorphosis. Although he had long ago stopped describing himself as a neo-socialist, he still claimed to be progressive. He was a Hillary Clinton supporter in 2016, and he had made clear to employees that he was personally repulsed by Trump. He had said, "I respect nothing about the dude. It would be hard to make up someone I find less appealing.” On certain issues, such as immigration, he expressed opinions that seemed consistent with a liberal worldview (at the same time, though, he opposed affirmative action and was a staunch supporter of the Second Amendment to the US constitution). 

But he thought progressives had been very irresponsible on the issue of immigration. He was increasingly unhappy over the role of identity politics and started drifting away from the left. In the meantime, Donald Trump (who he had criticized earlier, calling him "a phony billionaire") was running for president again, and Karp started warming to Republicans and to the idea of a second Trump presidency. He recognized that it was a huge opportunity for Palantir, being a major government contractor, if they played their cards right. 

Now he is a big Trump supporter, involved in ICE operations. He had quietly made a $1 million personal donation to the Trump-Vance Inaugural Committee. He published a book called The Technological Republic: Hard Power, Soft Belief, and the Future of the West whose main point is that a new, tech-driven nationalism was needed to keep America, and by extension the West, dominant. After an interview on CNBC, one of the cohosts commented that Karp was “an enigma wrapped in a riddle. He always emphasizes ‘I’m a progressive’ and then he "goes on to sound like just a huge right-winger.”

Palantir's platform was used with Anthropic’s Claude in the capture of Venezuela’s President Nicolás Maduro, according to the Wall Street Journal. Karp told CNBC that his company’s technology is being used in the war in the Middle East. He seemed frustrated that he couldn’t take more credit for the continued war being waged in Iran and made it clear that he supports President Donald Trump’s efforts. Palantir has experienced significant stock appreciation and high valuation multiples since the start of the conflict in the Middle East.

Palantir Technologies - I of IV

Palantir Technologies is a relatively small company, with only around four thousand employees but its reach is huge.  Climate change, famine, immigration, human trafficking, financial fraud, customs enforcement at ICE, the future of warfare - Palantir is at the center of many events that you see in the news. Under President Trump, Palantir has become an essential tool in American wars abroad and policy at home. Yet it has stayed largely under the radar.  

Its stock rose around 500% in the past 5 years. But it had a poor 2026 although it has risen again in the past few days. Palantir was one of the most expensive stocks on the market when its decline began, and even after its sell-off, it is still expensive at over 100 times forward earnings. Many think it will follow the same path as Nvidia, another company that benefited from the rise of artificial intelligence. And yet, unlike Nvidia, many people don’t know what Palantir does. There was a funny tweet that illustrated this point: 

“If someone held me hostage and asked me to explain what Palantir does, tell my family I love them…”

The company was founded by Peter Thiel (first major Facebook investor and founder of PayPal) and Alex Karp in 2003. Alex Karp is the chief executive officer. It was started after the 9/11 attacks in the US and was financed in part by In-Q-Tel, the CIA’s venture capital arm. A number of secret services now use Palantir, including the Mossad. All six branches of the U.S. military has deployed its technology. Palantir clients include the FBI, the IRS, and the National Institutes of Health, or NIH. It has become a major defense contractor. 

Alex Carp has a very unusual background for someone who is a big name in Silicon Valley. He grew up in Philadelphia in a very left-wing household, the son of a Jewish pediatrician and a black mother who's an artist. Much of his childhood was spent going to anti-war protest and he used to describe himself as a neo-socialist. He's biracial and he identified very strongly with his black heritage. He was someone who was sensitized to injustice both at home and abroad.

Karp majored in philosophy at Haverford. He went on to earn a law degree from Stanford University and a doctorate in social theory from Germany’s Goethe University, Frankfurt. He had no desire to pursue a career in academia, and when Peter Thiel, a law school classmate, asked Karp in 2003 if he would be interested in joining a start-up that was building software to fight terrorism, he jumped at the opportunity. Not long thereafter, Karp became Palantir’s CEO.  

Under Karp, Palantir became a dominant force in data analytics, a multibillion-dollar enterprise with swank offices around the world and an aura of intrigue that set it apart from other Silicon Valley companies. The company went public in 2020 and officially made Karp a billionaire. He became a center of attention at events like the World Economic Forum in Davos, Switzerland. Heads of state were eager to hear his thoughts, and he was in ever-greater demand as a speaker.

What exactly does Palantir do? It works with the raw data that has been collected by the various organizations it works with. Palantir doesn’t collect or store the data itself, and it doesn't sell data. This data collected by the various organizations is messy and riddled with mistakes, can be coded in different languages, such as Python or Java and can be stored in multiple databases that aren’t linked. There is also the problem of dealing with the huge volume of data that is generated now via phones, watches, satellites, automobiles, etc. 

Palantir produces software that enables organizations to pool the data they have which is tedious work if done manually. The software cleans up and standardizes the data and turns it into a composite dataset. Customers run queries to find patterns, correlations, trends, connections in that data that would take human analysts hours, days, even weeks to find. They typically work with large organizations that pull in massive amounts of data on a daily basis, like the US Army or Airbus.

It can be customized to reflect the particular needs and habits of mind that guide a corporation or a government agency and can be applied to a broad range of issues. For example, it has been used by the U.S. Centers for Disease Control and Prevention (CDC) to track food borne illnesses; by the German pharmaceutical company Merck KGaA to accelerate the development of new drugs and by the U.S. Securities and Exchange Commission (SEC) to combat insider trading.

The troubling legacy of Fritz Haber - V of V

Albert Einstein, already living abroad, observed Haber’s suffering but felt little sympathy. Einstein’s earlier disdain for all things German had hardened, under the influence of events, into fierce loathing. His letters to Haber display the satisfaction of a man who’d finally won a long-running argument. “I can imagine your inner conflicts,” he wrote to Haber in May 1933. “It is somewhat like having to abandon a theory on which you have worked for your whole life. It’s not the same for me because I never believed in it in the least.

Haber was plagued by depression, physical weakness, and a failing heart. He died in 1934, broken, unable to work in his native country which he had served so loyally, unable to work in another country since it was reluctant to accept him. The deepest tragedy in this was the fact that his destruction was, in part, self-destruction - he had led the pro-German chorus during the WWI. 

As the years passed, Haber’s work during World War I grew into a symbol of science’s uneasy conscience about its workings. Before Haber, soldiers had never relied so heavily on the latest products of science and industry. Never before had research institutes worked so closely with military leaders. Scientists and generals alike began to understand that their once-distant worlds were linked forever. Gas warfare became one symbol of this union. 

Haber represented the first of a breed. He was the forerunner of every modern scientist who works on banned weapons — at least those weapons, such as nuclear bombs, that international treaties allow in a few privileged nations but not in others. And the moral choices that he confronted during his life were not so different from those that we face today. He was not an evil man. His defining traits — loyalty, intelligence, generosity, industry, and creativity — have always been prized traits. 

Scientists abroad marveled at the German marriage of science and warfare, and rushed to imitate it. The United States set up a National Research Council and began a crash program to build nitrate factories of its own. It spent $100 million on them (about $1.6 billion today) by the end of the WWI which forged enduring links between universities and the military. Philosopher John Dewey called this interweaving of science and government policy a kind of borrowed “Prussianism” and predicted that it would remain even after the war had ended and so it has proved. Haber was the spiritual father of the military-industrial complex. 

Some time after the war, Haber's institute had made an insecticide called Zyklon A, a cyanide-based crystal that turned into vapor when exposed to air. Haber helped arrange funding for their laboratory. Later the concerned scientists moved to another laboratory where they upgraded it to Zyklon B. After Haber’s death, came the horrors of WWII. The Nazis built human-scale gas chambers and used Zyclon B as a tool of death on a scale beyond all normal imagination. Members of Fritz Haber’s extended family, children of his sisters and cousins, were hauled to those camps and killed by a gas their famous relative had helped develop.

If German politics had turned out differently, Fritz Haber might have been considered a hero, and statues of him might now stand in prominent places. Instead, Haber became a tragic figure. Haber's motivations may seem misguided, but before we rush to condemn, we have to remember that most of us behave in the same way. Most people, now as then, swim with the current of public sentiment; most embrace technical progress; most support their homelands. Haber too was guided by these motivations but his superior intelligence and drive meant that he went further and more dramatically than most. 

Haber embodied the capacity of science to nourish life and destroy it. The legacy of this forgotten scientist is present in every day’s news headlines and in every bite of food. Nitrogen is essential in war and in peace and the chemical reaction that Haber discovered delivered unlimited quantities of it. You could say that Haber snatched bread and bombs from the atmosphere. Ultimately the same person who saved billions of lives is also responsible for the deaths of millions of people. 

The institute in Berlin that once was Fritz Haber’s domain bears his name: The Fritz Haber Institute of the Max Planck Society. The name is mildly controversial at the institute; occasionally someone suggests that it be changed. Matthias Scheffler, one of the institute’s five directors,  prefers to keep the name. It reminds every scientist at the institute that knowledge can be a tool for good and for evil, for creation and destruction. A high school in Berlin that once bore Haber’s name did drop it a few years ago.  In Master Mind: The Rise and Fall of Fritz Haber, the Nobel Laureate Who Launched the Age of Chemical Warfare, Daniel Charles says about why most people tend to ignore his memory: 

The reason, I suspect, is that he fits no convenient category. Haber was both hero and villain; a Jew who was also a German patriot; a victim of the Nazis who was accused of war crimes himself. Unwilling to admire him, unable to condemn him, most people found it easier to look away.

Clara Immerwahr, Fritz Haber's first wife, has found fame in recent decades. The Clara Immerwahr Award launched by UniSysCat (Unifying Systems in Catalysis) in 2011, is an award for promoting equity and excellence in catalysis research fostering young female scientists at an early stage of their career. Haber's institute, The Fritz Haber Institute of the Max Planck Society had a memorial built for Clara in the garden of the institute in 2006.