Daily Archives: February 19, 2017

AI can beat humans in chess, Go, poker and Jeopardy. But what about emotional intelligence or street smarts?

Artificial intelligence has gotten pretty darn smartat least, at certain tasks. AI has defeated world champions in chess, Go, and now poker. But can artificial intelligence actually think?

The answer is complicated, largely because intelligence is complicated. One can be book-smart, street-smart, emotionally gifted, wise, rational, or experienced; its rare and difficult to be intelligent in all of these ways. Intelligence has many sources and our brains dont respond to them all the same way. Thus, the quest to develop artificial intelligence begets numerous challenges, not the least of which is what we dont understand about human intelligence.

Still, the human brain is our best lead when it comes to creating AI. Human brains consist of billions of connected neurons that transmit information to one another and areas designated to functions such as memory, language, and thought. The human brain is dynamic, and just as we build muscle, we can enhance our cognitive abilitieswe can learn. So can AI, thanks to the development of artificial neural networks (ANN), a type of machine learning algorithm in which nodes simulate neurons that compute and distribute information. AI such as AlphaGo, the program that beat the world champion at Go last year, uses ANNs not only to compute statistical probabilities and outcomes of various moves, but to adjust strategy based on what the other player does.

Facebook, Amazon, Netflix, Microsoft, and Google all employ deep learning, which expands on traditional ANNs by adding layers to the information input/output. More layers allow for more representations of and links between data. This resembles human thinkingwhen we process input, we do so in something akin to layers. For example, when we watch a football game on television, we take in the basic information about whats happening in a given moment, but we also take in a lot more: whos on the field (and whos not), what plays are being run and why, individual match-ups, how the game fits into existing data or history (does one team frequently beat the other? Is the quarterback passing for as many yards as usual?), how the refs are calling the game, and other details. In processing this information we employ memory, pattern recognition, statistical and strategic analysis, comparison, prediction, and other cognitive capabilities. Deep learning attempts to capture those layers.

Youre probably already familiar with deep learning algorithms. Have you ever wondered how Facebook knows to place on your page an ad for rain boots after you got caught in a downpour? Or how it manages to recommend a page immediately after youve liked a related page? Facebooks DeepText algorithm can process thousands of posts, in dozens of different languages, each second. It can also distinguish between Purple Rain and the reason you need galoshes.

Deep learning can be used with faces, identifying family members who attended an anniversary or employees who thought they attended that rave on the down-low. These algorithms can also recognize objects in contextsuch a program that could identify the alphabet blocks on the living room floor, as well as the pile of kids books and the bouncy seat. Think about the conclusions that could be drawn from that snapshot, and then used for targeted advertising, among other things.

Google uses Recurrent Neural Networks (RNNs) to facilitate image recognition and language translation. This enables Google Translate to go beyond a typical one-to-one conversion by allowing the program to make connections between languages it wasnt specifically programmed to understand. Even if Google Translate isnt specifically coded for translating Icelandic into Vietnamese, it can do so by finding commonalities in the two tongues and then developing its own language which functions as an interlingua, enabling the translation.

Machine thinking has been tied to language ever since Alan Turings seminal 1950 publication Computing Machinery and Intelligence. This paper described the Turing Testa measure of whether a machine can think. In the Turing Test, a human engages in a text-based chat with an entity it cant see. If that entity is a computer program and it can make the human believe hes talking to another human, it has passed the test. Iterations of the Turing Test, such as the Loebner Prize, still exist, though its become clear that just because a program can communicate like a human (complete with typos, an abundance of exclamation points, swear words, and slang) doesnt mean its actually thinking. A 1960s Rogerian computer therapist program called ELIZA duped participants into believing they were chatting with an actual therapist, perhaps because it asked questions and unlike some human conversation partners, appeared as though its listening. ELIZA harvests key words from a users response and turns them into question, or simply says, tell me more. While some argue that ELIZA passed the Turing Test, its evident from talking with ELIZA (you can try it yourself here) and similar chatbots that language processing and thinking are two entirely different abilities.

But what about IBMs Watson, which thrashed the top two human contestants in Jeopardy? Watsons dominance relies on access to massive and instantly accessible amounts of information, as well as its computation of answers probable correctness. In the game, Watson received this clue: Maurice LaMarche found his inner Orson Welles to voice this rodent whose simple goal was to take over the world. Watsons possible answers and probabilities were as follows:

Pinky and the Brain: 63 percent

Googling Maurice LaMarche quickly confirms that he voiced Pinky. But the clue is tricky because it contains a number of key terms: LaMarche, voiceover, rodent, and world domination. Orson Welles functions as a red herringyes, LaMarche supplied his trademark Orson Welles voice for Vincent DOnofrios character in Ed Wood, but that line of thought has nothing to do with a rodent. Similarly, a capybara is a South American rodent (the largest in the world, which perhaps Watson connected with the take over the world part of the clue), but the animal has no connection to LaMarche or to voiceovers unless LaMarche does a mean capybara impression. A human brain probably wouldnt conflate concepts as Watson does here; indeed, Ken Jennings buzzed in with the right answer.

Still, Watsons capabilities and applications continue to growits now working on cancer. By uploading case histories, diagnostic information, treatment protocols, and other data, Watson can work alongside human doctors to help identify cancer and determine personalized treatment plans. Project Lucy focuses Watsons supercomputing powers on helping Africa meet farming, economic, and social challenges. Watson can prove itself intelligent in discrete realms of knowledge, but not across the board.

Perhaps the major limitation of AI can be captured by a single letter: G. While we have AI, we dont have AGIartificial general intelligence (sometimes referred to as strong or full AI). The difference is that AI can excel at a single task or game, but it cant extrapolate strategies or techniques and apply them to other scenarios or domainsyou could probably beat AlphaGo at Tic Tac Toe. This limitation parallels human skills of critical thinking or synthesiswe can apply knowledge about a specific historical movement to a new fashion trend or use effective marketing techniques in a conversation with a boss about a raise because we can see the overlaps. AI cant, for now.

Some believe well never truly have AGI; others believe its simply a matter of time (and money). Last year, Kimera unveiled Nigel, a program it bills as the first AGI. Since the beta hasnt been released to the public, its impossible to assess those claims, but well be watching closely. In the meantime, AI will keep learning just as we do: by watching YouTube videos and by reading books. Whether thats comforting or frightening is another question.

Excerpt from:

If I Only Had a Brain: How AI 'Thinks' - Daily Beast

By Jackson Grigsby, Harvard Class of 2020

On Thursday, February 16th, the JFK Jr. Forum at the Harvard Institute of Politics hosted a conversation on the past, present, and future of Artificial Intelligence with Harvard Kennedy School Professor of Public Policy Iris Bohnet, Harvard College Gordon McKay Professor of Computer Science Cynthia Dwork, and Massachusetts Institute of Technology Professor Alex Sandy Pentland.

Moderated by Sheila Jasanoff, Kennedy School Pforzheimer Professor of Science and Technology Studies, the conversation focused on the potential benefits of Artificial Intelligence as well as some of the major ethical dilemmas that these experts predicted. While Artificial Intelligence (AI) has the potential to eliminate inherent human bias in decision-making, the panel agreed that in the near future, there are ethical boundaries that society and governments must explore as Artificial Intelligence expands into the realms of medicine, governance, and even self-driving cars.

Some major takeaways from the event were:

1. Artificial Intelligence offers an incredible opportunity to eliminate human biases in decision-making

In the future, Artificial Intelligence can be utilized to eliminate inherent human biases that often influence important decisions surrounding employment, government policy, and even policing. At the event, Professor Iris Bohnet stated that every person has biases that inform their decisions. These biases can affect whether a candidate for a job is chosen or not. As a result, Bohnet suggested that by using algorithms, employers could choose the best candidates by using AI to focus on the candidates qualifications rather than by basing decisions on gender, race, age or other variables. However, the panel also discussed the fact that even algorithms can have bias. For example, the algorithm that is used to match medical students with residency hospitals can either be biased in favor of the hospitals preferences or the students. It is up to humans to control bias in the algorithms that they use.

2. Society must begin having conversations surrounding the ethics of Artificial Intelligence

Due to the fact that Artificial Intelligence is becoming more popularly utilized, society and governments must continue to have conversations addressing ethics and Artificial Intelligence. Professors Alex Pentland and Cynthia Dwork stated that as Artificial Intelligence proliferates, moral conflicts can surface. Pentland emphasized that citizens must ask themselves is this something that is performing in a way that we as a society want? Pentland noted that our society must continue a dialogue around ethics and determine what is right.

3. Although Artificial Intelligence is growing, there are still tasks that only humans should do

In the end, the experts agreed, there are tasks and decisions that only humans can make. At the same time, there are some tasks and decisions that could be executed by machines, but ultimately should be done by humans. Professor Bohnet emphasized this point by reaffirming humanitys position, concluding, There are jobs that cannot be done by machines.

Check out video of the full forum below:

See the rest here:

Artificial Intelligence & Bias - Huffington Post