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State of AI 2020 – Article by Pavel Ilin

State of AI 2020 – Article by Pavel Ilin

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Pavel Ilin


This summary is prepared based on the State of AI Report 2020, which was crafted by Nathan Benaich and Ian Hogarth.

The AI industry is very diverse in its application, and it’s going through a transformation from the magical-wand stage to the plateau of adequate development. Let’s take a look at what is happening in the AI industry.

Research

We haven’t come up with new super-smart algorithms. Progress in model performance keeps being driven by big computational budgets and huge data sets. Training of the GPT-3 language model, with its 175 billion parameters, cost approximately $10 million. At the same time larger models require less data to achieve the same level of performance. With a deep-learning approach we are getting close to the point when the cost of training will grow outrageous with incrementally smaller improvements of the model.

An important fact is that the code base of most artificial intelligence systems remains closed. Only 15% of papers publish their code. This raises a lot of concerns about reproducibility and AI safety. AI explainability remains a critical issue for AI safety research; there are promising avenues of exploration such as Asymmetric Shapley Values, but so far it’s unknown how AI systems make decisions. 

Natural language processing (NLP) models successfully simulate common scenes and linguistics, but they fail dramatically with understanding problems and context and forming knowledge. 

Talent

Talented people with skills in math and computer science are the drivers of the progress in the AI field. More and more US professors are being recruited by tech companies. This affects the quality of education that US universities can provide. We already can see a decline in the level of entrepreneurship among recent graduates. At the same time Universities are creating AI-related degree programs.

The US keeps its position as the main attractor of talented individuals. For example China contributes to the talent pool of AI developers, but after publication of their first results, talented people are most likely to move to the US. 90% of international PhD graduates stay and work in US universities and corporations. Demand for AI talent remains much higher than supply, even despite COVID-19’s impact on market growth.  

Industry

AI keeps progressing not only on a theoretical and research level. Many real world applications are already in use, and they are affecting the industries in various ways.

New drugs are being designed by AI, and they are already in clinical trials. For example AI-designed drugs for OCD treatment are out for testing in Japan. AI drug-discovery startups keep raising funds. Also big pharma is teaming up with startups around preserving privacy during drug discovery. For example OpenMined uses federated learning to preserve privacy with medical data. Viz.ai presented the first product which was approved by the Centers for Medicare and Medicaid Services in the US. Their product analyzes tomography scans and alerts specialists who can treat patients before they receive damage that leads to the long-term disability. 

Progress in self-driving cars stays limited. Only 3 companies in California have permission to conduct testing of self-driving cars without a safety driver. Self-driving mileage remains microscopic compared to human drivers (2,874,950 miles for self-driving cars versus 390,313,739,000 miles for humans). The research and development process for self-driving cars remains very expensive. The major companies in this field raised around $7 billion since July 2019. Tesla chose to approach gradually adding self-driving features to its cars, but human drivers still remain in the loop. Recent approaches such as supervised learning do not perform well enough. To make dramatic breakthroughs, new approaches are required.

Computer vision unlocks faster accident and disaster recovery intervention. It also reduces the amount of human hours spent using a microscope, which could lead to acceleration of development processes and reduction of product costs.

AI drives sales and at the same time reduces costs in supply chains and manufacturing. Robotic process automation and computer vision are the most commonly deployed techniques in the enterprise. Speech, natural language generation, and physical robots are the least common. Recently IBM partnered with health insurance company Humana. IBM implemented natural language understanding (NLU) software which is already live and handles calls. It not only redirects calls to the different queues; it’s able to answer basic questions, such as “How much will the copay be to visit a specific specialist?” without human intervention.

Modern AI, in order to perform well, requires a lot of computing resources. Specialized AI hardware keeps progressing, and companies are now presenting second generations of their products. Graphcore M2000 offers faster training time to drop the cost of state-of-the-art models. Google’s new TPU v4 delivers up to a 3.7x training speedup over their TPU v3. NVIDIA will not rest either; it has achieved up to 2.5x training speedups with the new A100 GPU vs V100. Increasing interest towards machine learning devOps is a signal that the industry shifting its focus from how to build models to how to run them.

Despite the COVID-19 pandemic, investments keep coming into the industry. Private funding rounds of greater than $15 million for the AI-first companies remain strong.

Politics

Usage of AI for facial recognition tasks is extremely common around the world. Around half of the world allows facial recognition. This has become a recognizable political and ethical problem, especially when use of this technology leads to the wrong arrests. There were two highly publicized cases of wrong arrest in the US (which is probably just a tip of the iceberg). In May 2019, Detroit police arrested Michael Oliver who was wrongly accused of a felony for supposedly reaching into a teacher’s vehicle, grabbing a cellphone and throwing it, cracking the screen, and breaking the case. In January 2020, Detroit police arrested Robert Williams as a shoplifter who allegedly stole five watches from Midtown’s trendy Shinola store in October 2018. In both cases charges were dismissed but harm was done. 

Industry took a more thoughtful approach as a reaction to the AI mistakes. Microsoft deleted its database of 10 million faces, Amazon announced a one-year pause on letting the police use its facial recognition tool Rekognition. IBM announced it would sunset its general purpose facial recognition products. Washington State in the US introduced requirements to acquire warrants to run facial recognition scans. The ImageNet, a popular image database, is making an effort toward reduction of the biases in its image collections.

As Deep Fake technology produces more and more realistic media, it becomes illegal to use in certain states in the US. California passed a law, AB 730, aimed at deep fakes, which criminalizes distributing audio or video that gives a false, damaging impression of a politician’s words or action. Many other US state bills have been passed, addressing different risks. For example Virginia law amends current criminal law on revenge porn to include computer-generated pornography.

The US government keeps pursuing implementation of the military AI systems. DARPA organised a virtual dogfighting tournament where various AI systems would compete with each other and a human fighter pilot from the US military.

AI nationalism is on the rise. Countries tend to pursue protectionist policies to scrutinize acquisitions of AI companies by the players from other countries.

Every year AI plays a more and more noticeable part in our lives. It becomes cheaper, and you learn how to do new things. But we have to remember that at the moment AI is still a tool. And there are some philosophical and methodological difficulties which we have to overcome before it will be possible to deliberate about the potential sentience of the AI. It’s very important for the policy makers to make informed decisions based on how technology actually works and not on magical understanding formed based on popular sci-fi.

Pavel Ilin is Secretary of the United States Transhumanist Party. 

 

Othering: The Ultimate Challenge of Sentient Life – Article by Pavel Ilin

Othering: The Ultimate Challenge of Sentient Life – Article by Pavel Ilin

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Pavel Ilin


We come to the point in history when compassion and belonging become not only a necessary part of building inclusive culture, but the practices which determine the survival of sentient life as we know it. 

Most of us are locked in our local tribes – national, ideological, corporate, and so on. Living in different tribes is a way to establish one’s own subjectivity and identity. Most of the time we are part of the different tribes at the same time. The problem comes when we are not engaged in cross-tribal dialog and consider members of other tribes as “Other”. That’s how othering happens.

Most commonly, othering expresses the boundaries between “we” and “them”. “We” belong; “they” are Other and cannot belong. Those who are “them” can be described in the negative language of disgust.

When people say “they are barbarians”, they deny the right of the other to belong. When Ancient Greeks called slaves “speaking tools”, or they allowed only males to vote, they denied the others’ humanity and right to belong. When Romans expand their empire, they called other nations they conquered barbarians, unless they adapted the Romans’ culture – and by doing so the Romans othered these peoples. We can go on and on. The history of sentient life as we know it is full of othering.

When we deny the opportunity for the other to belong, then the other is not a person anymore in our eyes. You are allowed to do things to an “Other”, which would be unthinkable to do to one of “Us”. It’s very easy to deny access to resources, deny rights, deny compassion, and deny life to the “Other”. Taking away life is an extreme form of othering.

Personal-Level Othering

On the personal level othering happens very often. Susan Fiske from Princeton University shows in her “Stereotype content model” how othering works on neurological level. Studies show that when we see other people, there is a part of our brain that lights up when we see another human being. It’s an interesting authentication mechanism which allows us to identify our own species. But when we see, for example, homeless people, or undocumented immigrants and refugees, this part of the brain doesn’t light up. Instead, there lights up a part of the brain responsible for fear and disgust. 

Studies also show that if we put different people in situations when they have to work together to achieve a common goal, othering tends to diminish. When people engage in dialog, when people communicate and coordinate, people start to see each other, and neural activity gets changed. The person who previously provoked a fear response becomes a human again.

The moral here is that in order to overcome othering on a personal level, we should collaborate with as many people as we can.

Structural-Level Othering

But it’s not enough to deal with othering on a personal level. We live in social structures which determine our behavior and our identity. 

For example, the US government calls all immigrants “Alien”. And by doing that on a structural level, the government said to the immigrants that “you do not belong”. 

When police profile people based on how they look, it’s othering on a structural level.

When we allow such a huge level of income inequality, it’s a structural problem. The whole system of resource distribution is organized in a way that it others the poor and favors the rich.

What to do with structural othering? Create an inclusive structure which allows people to belong. It sounds easier than it is, but this way we can upgrade our culture.

Implementing basic income, removing immigration barriers, providing access to educational resources to all are all important steps. It is also vital to create organizations within which belonging is cultivated. The USTP is a great example of such an organization. Everyone is welcome to participate regardless of residence, income level, and a variety of other circumstances or attributes. You are welcome in the USTP even if you are not human or not an organic-based form of sentient life.

The Future

Why is it important to talk about othering in the context of the building of transhumanists’ future?

In context of the COVID-19 situation, it’s become very clear that we can’t hide inside our small narratives. It’s impossible to stay away from the pandemic situation. In order to get over this pandemic with minimal loss of human lives, we have to work together. It’s not going to be the last pandemic, and in order to prepare for future threats, we have to cooperate more closely. A virus is only one of the potential existential threats. What about climate change, bio-terrorism, asteroids, and destructive AI? We have to work together to survive!

As transhumanists we should prepare ourselves to face the “other” and to become “other” in certain situations as well. I think we need to create structures which can be inclusive not only for humans but for all sentient beings.

Other Modified Beings

There are millions of people around the world with artificial body parts and this number will keep growing. How will the culture react when organization goes from regaining physical ability to enhancing ability? We can face creating a new other and potentially new discrimination. 

Uplifted Animals

Recognizing that animals are living beings with feelings and thoughts is pretty new phenomenon. And mostly right now animals are considered commodities. We will have to have a very difficult dialog about humans behavior with animals uplifted to human-level intelligence.  

Other AI Entities

While we create Artificial Intelligence, we should remember that creating tech slaves is not going to end anywhere good. We should create colleagues with whom we are going to work to build a transhumanist future. We might even become artificially intelligent forms of life ourselves. 

Conclusion

But race is the child of racism, not the father. – Ta-Nehisi Coates

I believe that othering is an existential issue. If we keep dividing people and other beings into “us” and “them”, then we will keep facing discrimination, conflicts, and wars. This is extremely concerning for me because we have now a lot of technological power at our disposal, and it keeps growing – and with great power comes great responsibility. What I mean is that we can easily destroy our planet by just pressing the nuclear button. And if we consider people in other countries or in other social groups as Others, if they are outside the circle of our concern, then it’s very easy to deny help and start wars.

We have built virtual walls between us, and I think it’s time to tear them down by reclaiming and living and practicing belonging where no beings and lives are outside of the circle of concern.

Pavel Ilin is the Secretary of the U.S. Transhumanist Party.