There is a lot of AI in medicine, mainly used for diagnosis and medical image processing. For example, diagnosis of pancreatic cancer (the one that killed Steve Jobs) from computed tomography imaging.
Among recent demos of AI in medical field the one in this video is perhaps the most spectacular.
There are a lot of hopes for future application of Watson in medicine. Here is the video about it I posted before:
Good luck with your presentation.
Glad it helped. I haven’t been updating AI blog for a while. You just inspired me to do it.
In a cross between Google Plus and Gmail, Comedivia brings the world G-Male, the perfect male boyfriend by Google. He knows all your interests and you will never forget anything again with him around, plus he is a really good listener. I am sure he will get along good with the Gmail Man.
Artificial intelligence at its best! G-Male is the project of Google to create a humanoid personal assistant/partner that learns, predicts and does what you need. No kidding:)
There is a strong connection between evolution, learning and intelligence. In this short piece I’ll try to track this connection for biological organisms and project it to machine intelligence.
The concept of intelligence is tricky to define. What is clear, however, is that learning is the crucial feature of intelligence. There are hardly any examples of biological organisms that are considered intelligent but unable to learn. Even bacteria can learn. The question is why learning is so essential? An obvious answer is adaptation. The world is changing and in order to survive the appropriate modifications in behaviour have to be made. The same function is fulfilled by evolution but it takes much more time than a life of an individual organism. Nevertheless, it works for microorganisms with short lifespan. Larger creatures live longer and evolution is combined with learning to achieve the sufficient rate of adaptation.
What about machines? Do we need learning, evolution or both to achieve intelligence in machines? The answer is much less obvious than with biological organisms. Unlike nature, learning and evolution implemented on a computer can be used to achieve similar goals. There is no binding to the reproduction rate or lifespan of an organism, a computer can run through millions of generations to come up with the optimal design for a robot in seconds. Computer simulation is often sufficient so there is no need for building millions of physical robots from intermediate generations. Artificial evolution can alter the behaviour of a robot during it’s ‘life’. For example, evolutionary computations can be used to construct and modify plans for a robot to accomplish a task. The same goes for machine learning, which may have other effects than it is possible in nature. For example, a robot may learn to modify its physical body according to the task at hand. Another interesting example which can be easily implemented on a computer but impossible in nature is Lamarckian inheritance i.e. learned skills and knowledge acquired during lifetime transferred to offspring. Intuitively, this kind of inheritance might be very desirable for evolving highly intelligent machines.
Modern intelligent machines such as well-known question answering system Watson and chess computer Deep Blue although regarded as intelligent are far from being biologically plausible. They utilize machine learning and evolutionary computations merely as tools to accomplish specific subtasks. Until now this approach was arguably more successful than attempting to copy nature and I tend to believe that it will stay this way.
You are welcome to express your opinion on the topic in comments.