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Herbert Simon of Carnegie-Mellon, Tom McCarthy, and others are credited with having founded the field of artificial intelligence (AI) on the claim that human intelligence can be so precisely described that a machine can be made to simulate it". According to computer scientists Stuart Russell and Peter Norvig, the term artificial intelligence" is applied when a machine mimics cognitive" functions that humans associate with other human minds, such as learning" and problem solving". The goal of machine learning" is that an ideal AI computer program should then have the ability to change itself to take actions that maximize its chance of success at performing a task.

Sharp minds are at work at places such as Alphabet (Googles) Deep Mind who claim that their final goal is to reach artificial general intelligence", or AGI. Others talk about the concept of a singularity", the moment in time when AGI becomes smart enough to be an intelligence that can make itself better without human intervention. In other words, machine learning will completely take control away from its human creators.

While I defer to these great minds, I would still argue that both those concepts are hollow. The danger lies in assuming that AGI is human intelligence, since human intelligence is not general". Learning is not intelligence. To expect that learning machines can make themselves more intelligent (as opposed to more efficient at performing tasks) is farfetched.

In my opinion, all that AI has been able to do until now is take algorithmic concepts that have long been known, and efficiently apply these to large volumes of data. To be specific, these fall in the areas of pattern-matching and predictive analyses. Hence, the term data scientist" and the huge number of job openings for those who understand some of the basic concepts of statistics, such as regression analysis that checks how variables are linked, the Box-Jenkins model that studies data in a time series, and the Bayes equation used for estimating the probability of various outcomes.

Philosophers have held that while tasks can be automated, there is one thing that a soulless machine can never do, and that is having living consciousness". If you doubt this, then simply ask yourself who is listening to these words as you read them to yourself. Is it your human learning" neurons, or some other, larger field of consciousness into which words and thoughts like these come and go and are understood? If a voice arises in your head that disagrees with what you are reading, who is it that is aware of the voice?

It would behoove us then to better understand where consciousness exists. I recognize that I am now venturing onto thin ice. There is no consensus between philosophers and scientists or indeed within academia on what consciousness is. The one thing everyone does agree on is that the phenomenon does, in fact, exist. Nonetheless, in a recent article in Scientific American, Christof Koch cites neurological studies that seem to have established that conscious awareness exists in the highly integrated and complex cerebral cortex in our brains, and is not to be found in the more primitive cerebellum, which governs our motor activities. Mankind has the largest cerebral cortex relative to other forms of life on earth. According to Koch, very little happens to consciousness if a cerebellum has been operated upon by a surgeon. This is because the cerebellum, unlike the cerebrum, is exceedingly uniform and parallel.

So far, so good. The finding relates to many philosophical schools of thought that say that man is the most evolved and sentient of all beings, at least on earth, and is the only animal capable of realizing that he, in fact, possesses the faculty of consciousness. Eastern philosophers would have it that the recognition of this consciousness as being both a limited aspect as well as the full expression of an all-pervading universal consciousness is the (spiritual) goal of life.

Switching to consciousness in information technology, there are two rival schools of thought, one called the global neuronal workspace (GNW), and the other called integrated information theory (IIT), posited by Koch and his collaborators. GNW holds that consciousness rises from information being processed in a specific manner. It says that AI programs process a sparse, shared repository of information; all the while, this information is also concurrently shared by a host of subsidiary processes in the system. According to GNW, once such a sparse set of information leaves the AI programs processing space and is replaced by another set of sparse information, the new information can also simultaneously be broadcast to the subsidiary processes that can suo motu make changes to handle their own subsidiary tasks. It is at this point, according to GNW, that the information becomes conscious".

In contrast, IIT has an outside-in" view of consciousness, since it starts at the experience and works backwards from there to find the conscious experiencer". Each experience is unique and exists only for the experiencer. IIT theorists postulate that any complex and interconnected mechanism whose structure encodes a set of cause-and-effect relationships will have these properties, and so will have some level of consciousness. In other words, it will feel like something from the inside. However, if the mechanism is anything like our cerebellum, it would lack integration and complexity, and will not be aware of anything. IIT says that consciousness is an intrinsic causal power associated with complex mechanisms such as the cerebral cortex. Programming for consciousness will never create a conscious computer.

So, consciousness cannot be computed; it has to be built into the structure of the system. This will take decades, as we still need to observe and probe the vast groups of highly heterogeneous and dissimilar neurons that make up the cerebral cortex of our brain to further isolate and understand the precise signifiers of consciousness. It will be quite a while yet before we have a Frankensteins monster to deal with.

Siddharth Pai is founder of Siana Capital, a venture fund management company focused on deep science and tech in India

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Opinion | Frankenstein monsters will not be taking our jobs anytime soon - Livemint

The articles of impeachment under consideration by the House clearly allege high crimes and misdemeanors under the Constitution. Apart from the factual truth of allegations, the articles comport with the definition of impeachable conduct. Start with abuse of office for personal advantage or gain, directly aimed at distorting the electoral process. For the Framers, this conduct was the classic form of a high crime or misdemeanor.

Their words demonstrate as much. At the Constitutional Convention in 1787, George Mason of Virginia warned of the danger that presidential electors could be corrupted by the candidates. Corruption meant the conferral of improper benefits for personal gain. James Madison worried about the presidency being used for a scheme of peculation, in other words, self-dealing or embezzlement for personal advantage.

What is more, the two impeachment trials best known to the Framers both involved abuse of office for improper personal gain. Warren Hastings, who was the former British governor general of Bengal, was undergoing his impeachment even as the Framers met in Philadelphia. George Mason referred to Hastings at the convention. Hastings was impeached for, among other things, corruption, peculation, and extortion.

The basic claim against him was that he had solicited and received bribes and gifts from people in Bengal while in office as governor general. Lord Macclesfield, who was the treasurer of England, was impeached in 1725, for taking payments to sell offices. The articles of impeachment charged Lord Macclesfield with seeking personal gain under color of office, that is, while he occupied the official role of the treasurer of England.

The takeaway from this historical evidence is that abuse of office for personal gain is the archetype of a high crime and misdemeanor under the Constitution. Thus, if the facts show that President TrumpDonald John TrumpRepublicans consider skipping witnesses in Trump impeachment trial Bombshell Afghanistan report bolsters calls for end to 'forever wars' Lawmakers dismiss Chinese retaliatory threat to US tech MORE sought his own personal gain when he solicited Ukrainian President Volodymyr Zelensky to announce investigations of the Bidens and of Crowd Strike, then he has undoubtedly committed an impeachable offense.

As for obstruction of Congress, the violation lies in President Trump issuing a blanket denial of the authority of Congress to engage in its impeachment investigation and his direction to all executive branch officials not to appear before Congress or cooperate. The precedent for considering this conduct lies in the article of impeachment adopted by the House Judiciary Committee charging Richard Nixon with obstruction of Congress. However, even President Nixon did not entirely stonewall Congress. In fact, he partially cooperated with the congressional inquiry. The charge against President Trump is for conduct that exceeds that of President Nixon when it comes to obstruction of Congress.

Under constitutional logic, the basis for this article of impeachment is fundamentally about the separation of powers. The Constitution gives the House of Representatives the sole power of impeachment. That means the House holds the power and authority to oversee the conduct of the president to determine whether or not he should be impeached. Thus, by issuing a blanket refusal to cooperate in the impeachment investigation, President Trump denied the House its constitutional authority.

The only remedy under the Constitution for this denial of congressional authority is impeachment. Consider that the executive branch cannot be responsible for presidential oversight. Consider further that the judiciary likely could not compel presidential participation in an impeachment inquiry, since impeachment is a power conferred on Congress, not the judiciary. It follows that impeachment itself is the clearly defined remedy for presidential refusal of the granted impeachment authority.

If the president had the authority to reject an impeachment inquiry, he would literally be above the law. He would not be subject to control by the other two branches if he committed wrongdoing. The name the Framers had for such an unimpeachable officer was a monarch. Obstruction of Congress is impeachable under the Constitution because it undercuts the basic structure of democracy that is founded in this country.

Noah Feldman is the Felix Frankfurter Professor at Harvard Law School. He testified before the House Judiciary Committee on the impeachment of President Trump this month. He is a columnist for Bloomberg Opinion and hosts the Deep Background podcast. He also served as a law clerk for Supreme Court Justice David Souter and is the author of numerous books including The Three Lives of James Madison: Genius, Partisan, President.

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Feldman: Impeachment articles are 'high crimes' Founders had in mind | TheHill - The Hill

Leaders in the AI community came together to release the 2019 AI Index report today, an annual attempt to examine the biggest trends shaping the AI industry, breakthrough research, and AIs impact to society.

It also examines trends like AI hiring practices, private investment, AI research contributions by nation, researchers leaving academia for industry, and how much AI plays a role in specific industries. The report also notes strides in the reduction of the amount of time it takes to train AI systems and computing costs, two of the biggest hindrances to AI adoption rates.

In a year and a half, the time required to train a large image classification system on cloud infrastructure has fallen from about three hours in October 2017 to about 88 seconds in July, 2019, the report reads.

Some highlights:

The report is compiled by the Stanford Human-Centered AI Institute in collaboration with people from OpenAI. It originated in 2016 as part of AI 100, a century-long Stanford study of AIs progress and impact.

What we set out to do was to be religious about the quality and objectivity of the data, Stanford University professor emeritus and steering committee chair Yoav Shoham told VentureBeat in a phone interview.

Shoham has been on the AI Index steering committee since the beginning and acted as chair of a group that put the report together. Others include MIT economist Erik Brynjolfsson, Partnership on AI executive director Terah Lyons, and others from SRI International, Harvard University, OpenAI, and the McKinsey Global Institute.

The work is intended to help the general public to understand progress in the field and inform policymakers and business decision makers about how their country ranks compared to other nations.

Now in its third year, the report has three times more data sources than at its launch, authors told VentureBeat, and for the first time comes with a Global AI Vibrancy tool, a way to compare countries across 34 axes.

Shoham called it premature to make national AI rankings, as some previous works have done.

Its tempting to just do a ranking of countries, just measure some things, add a bunch of numbers, and say, you know U.S. is number one and China is number two, and what have you, he said. We didnt want to do that because when you do that, you distort things and theres so many dimensions you could look at. And eventually, its a good idea to have something like a ranking, but we think its way premature to do it.

The Global Vibrancy tool gives the choice to measure by overall numbers as well as per capita trends to recognize hot spots in places such as Israel, which produces more per capita deep learning research than any other country, or advanced AI leaders like Finland and Singapore.

Earlier this year a consultancy firm working with the United Nations determined roughly 30 nations currently have national AI strategies.

For example, according to Elseviers Scopus, which looks at publication rates for repositories like arXiv, Europe produces more AI research papers than any other part of the world, but Israel has the highest per capita deep learning research and the United States produces the most-cited AI research.

Corporate or industry affiliation with AI research is growing, and is most likely to occur in U.S., China, Japan, France, Germany, and the U.K.

Ten years ago, 20 years ago, all innovation happened in academia, and then industry picked up bits and pieces of it, perfected it and commercialized it. Thats no longer true. The lines are blurred and people cross over, Shoham said. I think the leading academic institutions are coming to terms that this is the new normal.

Though 60% of PhD candidates go to industry over academia today compared to 20% in 2004, academic research still outpunches government and corporate papers it makes up 92% of AI publications from China, 90% from Europe, and 85% from the U.S., according to the report.

The report also assesses progress in benchmarks and methods to track AI across disciplines like image classification and progress in methods to train AI systems for common use cases like translation or ActivityNet for event recognition in videos.

In some regards, Shoham says progress results are mixed, as some AI systems that achieve high results in a benchmark may prove to be more brittle than those results may indicate.

Shoham looks to work in conversational AI, his field of research, for an example. Some systems may perform well on a benchmark like Stanfords SQuAD question and answering test, but appear to be overfit to narrow tasks.

The thing is these are highly specialized tasks and domains, and as soon as you go out of domain, the performance drops dramatically and the committee knows it, Shoham said. Theres a lot to be excited about genuinely, including all these systems that I mentioned, but were quite far away from human level understanding of language right now. So we try to be nuanced about that in the report.

The report also cites instances of human-level performance by AI systems such as DeepMinds AlphaStar beating a human in Starcraft II and detection of diabetic retinopathy in images of eyes using deep learning.

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AI Index 2019 assesses global AI research, investment, and impact - VentureBeat