In this time, we have evolved so much that we don’t even need our body to do some jobs with the help of artificiall intelligence. Especially in computer science.
Already we are seeing various industries using AI in many types of applications; for example, Abrams noted that in the medical field, a computer can immediately provide an oncology physician a newly-presenting patient’s entire medical history, symptoms and suggested diagnoses. Additionally, the AI program can scan thousands of new pages of medical journal articles released each day and suggest any new information that may be relevant. In another example, AI can create a predictive model of water usage in areas suffering droughts, basing the model on visual recognition of trees, pools and roof leaks, and allowing city planners to consider recommendations or regulations directing consumer water consumption.
Also, AI can greatly assist the monitoring and maintenance of oil rigs, which can have as many as thousands of sensors although engineers usually look at less than 1% of that available data. But AI can ingest a rig’s design documentation and years of maintenance logs and trouble tickets, so an engineer investigating a problem has better information to assess the problem. “When humans are getting better answers, we have found they then can ask more and better questions,” Abrams said.
And If we look at chess, we can see Alpha Zero. The ultimate champion of all chess motors.
This is where the neural network comes in. AlphaZero’s neural network receives, as input, the layout of the board for the last few moves of the game. As output, it estimates how likely the current player is to win and predicts which of the currently available moves are likely to work best. The M.C.T.S. algorithm uses these predictions to decide where to focus in the tree. If the network guesses that ‘knight-takes-bishop’ is likely to be a good move, for example, then the M.C.T.S. will devote more of its time to exploring the consequences of that move. But it balances this “exploitation” of promising moves with a little “exploration”: it sometimes picks moves it thinks are unlikely to bear fruit, just in case they do.
When the AlphaGo Zero and AlphaZero papers were published, a small army of enthusiasts began describing the systems in blog posts and YouTube videos and building their own copycat versions. Most of this work was explanatory—it flowed from the amateur urge to learn and share that gave rise to the Web in the first place. But a couple of efforts also sprung up to replicate the work at a large scale. The DeepMind papers, after all, had merely described the greatest Go- and chess-playing programs in the world—they hadn’t contained the source code, and the company hadn’t made the programs themselves available to players. Having declared victory, its engineers had departed the field.
Sources: legalexecutiveinstitute.com, newyorker.com