Presser says his experiment shows that the GPT-2 system has many unexplored capabilities. A savant with a gift for chess. A later version of the same software astounded web designers when a developer briefly trained it to spit out code for displaying items on a web page, such as text and buttons. The AI generated appropriate code even though all it had to go on was simple descriptions like "red text that says 'I love you' and a button with 'ok' on it".
Clearly, it had got the basic gist of web design but only after surprisingly little training. One arena where AIs have long impressed is video games. There are countless anecdotes in the AI community about surprising things algorithms have done in virtual environments. Video game-like spaces are often where algorithms are tested and honed, to see just how capable they really are.
In , OpenAI made headlines with a video about a hide-and-seek game played by machine learning-controlled characters. To the researchers' surprise, seekers in the game eventually learned that they could jump on top of items and "surf" them to get access to the enclosures where the hiders were cowering.
In other words, the seekers learned to bend the rules of the game to their advantage. A strategy of trial-and-error can result in all kinds of interesting behaviours. But they do not always lead to success. Two years ago, DeepMind researcher Victoria Krakovna asked readers of her blog to share stories about times when AIs have solved tricky problems — but in unpredictably unacceptable ways. The long list of examples she collated is fascinating.
Among them is a game-playing algorithm that learned to kill itself at the end of level one — to avoid dying in level two. The objective of not dying in level two was achieved, just not in a particularly impressive way. Another algorithm discovered that it could jump off a cliff in a game and take an opponent with it to its doom. That gave the AI enough points to gain an extra life so that it could keep repeating this suicidal tactic in an infinite loop.
He says these are classic examples of "reward allocation" errors. When an AI is asked to accomplish something, it may uncover strange and unexpected methods of achieving its goal, where end always justifies the means. We humans rarely take such a stance. The means, and the rules that govern how we ought to play, matter. Togelius and his colleagues have found that this goal-oriented bias can be exposed in AI systems when they are put to the test under special conditions. In recent experiments , his team found that a game-playing AI asked to invest money at a bank would run to a nearby corner of the virtual bank lobby and wait to receive a return on the investment.
Togelius says the algorithm had learned to associate running to the corner with getting a financial reward, even though there was no actual relationship between its movement and how much was paid out. This, says Togelius, is a bit like an AI developing a superstition: "You got a reward or a punishment for something — but why did you get it? This is one of the pitfalls of "reinforcement learning", in which an AI ends up devising a wrong-headed strategy based on what it encounters in its environment.
The AI doesn't know why it succeeded, it can only base its actions on learned associations. A bit like early human cultures that began to associate rituals with changes in the weather, for example. Or, pigeons. In , an American psychologist published a paper describing an unusual experiment in which he placed pigeons in enclosures and gave food rewards to them intermittently. The pigeons began to associate the food with whatever they happened to be doing at the time — be it wing-flapping or performing a dance-like motion.
A new family of lifetime distributions is introduced via distribution of the upper record values, the well-known concept in survival analysis and reliability engineering.
Some important properties of the proposed model A1 MATH. We draw attention to some corrections in the section of "Applications of Jack's Lemma" which appear in the above-mentioned paper see 3. In this paper, the Klein - Gordon equation is generalized using the concept of the variational order derivative. We try to construct the Crank-Nicholson scheme for numerical solutions of the modified Klein- Gordon equation.
In this paper, we deal with discrete operators of sampling type. It is known that this type of operators are related to generalized sampling series and they have important applications. It has a capacity of 80 people for banquette, people for cocktail purposes. Adnan Menderes airport is both a domestic and international airport so many airline companies have scheduled flights to and from it. Our hotel can be reached by Havas Shuttle, Izban or taxi from the airport.
If you want we can pick you from airport up and transfer you to the hotel by a private vehicle for a reasonable amount of fee. On the other hand, there are scheduled intercity bus services from many cities to İzmir. Main Coach Station of İzmir is very close to our hotel and it is at minute ride by taxi.
When coming from Canakkale or Bergama please follow Bornova and Ege University signs, so you can reach hotel easily. Our hotel is at Bornova district of Izmir, located in the Aegean Region of Turkey and right within the campus area of Aegean University. Our hotel is located among pine trees on 10 acres area, so you can stay at a peaceful atmosphere where you can be away from rush and noise of the city, listen to the sound of nature and feel at home.
If you are looking for functional service, a quiet atmosphere and fully equipped facilities suitable for events of all sizes for your congresses, meetings, seminars and other organizations, Anemon Ege Hotel will be the right choice for you.
Our elegantly decorated facilities can be arranged in various settings. At Anemon Ege Hotel you are our guest but you will feel as if you are the owner.. With a capacity of seats our A la Carte restaurant and breakfast hall serves an exquisite world cuisine menu for your palatal delight.
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