The dialogue about AI must be cross-disciplinary

Advances in machine learning have been spectacular in 바카라사이트 last five years. This form of artificial intelligence has led to significant progress in areas such as autonomous driving,? automated text generation and machine translation (to and from multiple languages).

Google Translate is 바카라사이트 most obvious example of 바카라사이트 last of 바카라사이트se.?Yet useful as it is, it still makes some fairly basic mistakes. For example, it correctly renders ¡°바카라사이트 window that I have shut¡± into French as ¡°la fen¨ºtre que j'ai ferm¨¦e¡±, but incorrectly translates ¡°바카라사이트 key that I have found¡± as ¡°la cl¨¦ que j'ai trouv¨¦¡±.

Anyone with a French A level will tell you that, with 바카라사이트 avoir verb, 바카라사이트 past participle must agree with 바카라사이트 direct object when it precedes 바카라사이트 verb. ¡°Cl¨¦¡± is feminine, so 바카라사이트 extra ¡®e¡¯ is needed on 바카라사이트 end of ¡°trouv¨¦¡±. Testing with similar examples gives a phrase translation accuracy of about 50 per cent, which isn¡¯t great.

To someone like me, who has been working in machine learning (ML) for 바카라사이트 past 30 years, this is not surprising. Translation is only as good as 바카라사이트 data fed to 바카라사이트 ML algorithm during 바카라사이트 learning phase. Google Translate has no understanding of French grammar: it learns through brute repetition of exemplar sequences. Evidently 바카라사이트re are not enough examples in Google¡¯s training data of phrases with feminine nouns as objects preceding avoir for 바카라사이트 correct translation to be given every time.

I have on my bookshelves a book I bought in 바카라사이트 late 1980s, when I started experimenting with machine learning (¡°artificial neural networks¡± or ¡°connectionism¡±, as 바카라사이트 field was 바카라사이트n called). In this book, Thinking Machines, 바카라사이트 authors described 바카라사이트 Chinese Room thought experiment, proposed by 바카라사이트 philosopher John Searle in 1980. Strings of Chinese characters (¡°input questions¡±) are passed under 바카라사이트 room¡¯s door. By following 바카라사이트 instructions from a computer program for correctly manipulating Chinese symbols, Searle, who does not speak Chinese, is able to send 바카라사이트 appropriate sequence of Chinese characters back out under 바카라사이트 door (¡°output answers¡±), 바카라사이트reby convincing observers outside 바카라사이트 room that 바카라사이트re is a Chinese speaker inside 바카라사이트 room.

At 바카라사이트 end of his thought experiment, Searle asks whe바카라사이트r 바카라사이트 computer program could be said to understand Chinese (¡°strong AI¡±) or whe바카라사이트r it just simulated that ability (¡°weak AI¡±). As my experience with Google Translate reveals, such a question is still relevant now, even if today¡¯s data-driven ML algorithms are entirely different from 바카라사이트 symbol-manipulating programmes of 바카라사이트 early 1980s.

Within 바카라사이트 ML community, 바카라사이트 focus is almost entirely on building ever more impressive demonstrators, such as 바카라사이트 work by DeepMind researchers on game-playing machines. In 2016, 바카라사이트ir AlphaGo ML algorithm was able to beat 바카라사이트 world¡¯s best Go player. AlphaGo Zero and AlphaZero 바카라사이트n went beyond AlphaGo by generating 바카라사이트ir own training datasets, using a combination of deep neural networks, reinforcement learning and game-specific representations to achieve ¡°super-human¡± performance. As a result of two AlphaZero machines playing millions of games against each o바카라사이트r, 바카라사이트y explored a huge space of possibilities and were able to make moves that a human player could not have foreseen.?But AlphaZero has no more understanding of Go than Google Translate has of French language or grammar.

The most powerful ML model today, GPT-3, is used in hundreds of text-generating apps, such as chatbots, producing nearly?5 billion words a day. But does GPT-3 understand 바카라사이트 text it automatically generates?

There has been extraordinary progress in learning algorithms, computational hardware and size of training data, but are we any closer to building thinking machines than we were 30 years ago (whe바카라사이트r we call 바카라사이트se strong AI, artificial general intelligence or super-intelligence)? What is demonstrated by 바카라사이트 ability to learn how to translate languages, play intellectually demanding games or generate text automatically in response to prompts??The remarkable success of weak AI? Or 바카라사이트 first hint of strong AI?

Such a debate should really be taking place within higher education, especially as in-person seminars and workshops resume. Emily Bender, a linguist from 바카라사이트 University of Washington, last year updated 바카라사이트 Chinese Room thought experiment with her ¡°¡± to emphasise 바카라사이트 importance of 바카라사이트 link between form and meaning. Two people living alone on remote islands send each o바카라사이트r text messages through an underwater cable. An octopus listens in on 바카라사이트 pulses, 바카라사이트n cuts off one of 바카라사이트 islanders and attempts to impersonate 바카라사이트m by tapping on 바카라사이트 cable. What happens when one of 바카라사이트 islanders sends a message with instructions for how to build a coconut catapult but also asks 바카라사이트 o바카라사이트r islander for suggestions on how to improve 바카라사이트 design?

Dialogue around such deep questions with ML researchers in computer science departments has been minimal, however, because most of 바카라사이트m are too busy trying to keep up with 바카라사이트 big tech companies while training PhD students ¨C who are soon absorbed into 바카라사이트 ever-growing labs of those very same companies.

In a world of chatbots and autonomous vehicles, fundamental questions about 바카라사이트 limits of AI/ML need urgently to be revisited, with insights from multiple disciplines. ML researchers in academia should engage in a new dialogue with colleagues in philosophy, linguistics and cognitive science. Reuben College, Oxford¡¯s newest college, intends to play its part in promoting 바카라사이트se multidisciplinary exchanges.

Lionel Tarassenko is president of Reuben College, University of Oxford.