Artificial Intelligence is the subject of understanding the cognition process of humans specifically self-mind and then producing the methods with hard-computing as well as soft-computing rigorous notions to enable development of machines that work for humans.
AI can never be ignored. This is the backbone in development of Software Systems, Hardware Systems like smartphones to rocket launchers, Database Systems like google-search, Network Systems, and Web-Agent Systems. We experience several instantly when we wake up daily.
There is a broad opening in healthcare and Bio Medical development with smart support. That could be only by using AI techniques.
Usually readily available pre-proved techniques are used with confidence to develop any new system. Surface or Deep learning is a part of evolution. However we face AI revolution at every instance of our time.
To me "Artificial Intelligence could never be ignored".
Kind Regards.
Artificial Intelligence is the subject of understanding the cognition process of humans specifically self-mind and then producing the methods with hard-computing as well as soft-computing rigorous notions to enable development of machines that work for humans.
AI can never be ignored. This is the backbone in development of Software Systems, Hardware Systems like smartphones to rocket launchers, Database Systems like google-search, Network Systems, and Web-Agent Systems. We experience several instantly when we wake up daily.
There is a broad opening in healthcare and Bio Medical development with smart support. That could be only by using AI techniques.
Usually readily available pre-proved techniques are used with confidence to develop any new system. Surface or Deep learning is a part of evolution. However we face AI revolution at every instance of our time.
To me "Artificial Intelligence could never be ignored".
Kind Regards.
I Agree with Mr. Hussain.
However, limitations may be considered as less understanding of Nature. There are lot of Nature Inspired Algorithms available. If we can use we can leverage the benefit.
Future challenges, may be in the area of Agriculture where we can depot more smart technologies with the support of MET dept.
Deep learning is always required at any stage while learning without which one can not evolve.
Genetic Algorithms, ANN are few areas where I work and proved to be a strong and nature inspired algorithms for optimization problems.
In order to understand the foundations and challenges the following presidential address in American Association for Artificial Intelligence would be extremely helpful.
http://www.agent.ai/doc/upload/200310/redd88_1.pdf
Moreover, you must understand the Eight Challenges for Artificial Intelligence by Rodney Brooks (http://research.microsoft.com/en-us/um/people/horvitz/seltext.htm ).
For a more involved study refer to the following link.
http://www.springer.com/gp/book/9783319068824
I agree with previous comments.
In addition, I would like to say that deep learning was indeed a revolution in the artificial neural networks domain. Before 2006, the use of deep structures was abandoned in favor of shallow structures because their difficulty for training. This can be mainly explained by showing the performance of the back-propagation approach with a large number of hidden layers from random initialization. In 2006, the concept of Greedy Layer-Wise Learning was introduced, which means that each layer is trained at a time to initialize all the parameters, and once all the layers are trained, the whole system is trained to fine-tune those parameters. This introduction has contributed to the increasing use and development of a lot of deep learning approaches during the last 8-9 years. That is why Deep learning was a revolution in the machine learning field.
From my point of view, one of the future challenges will be to combine different AI-techniques to allow a broader use. Currently there is a number of AI-techniques, each one for a specific purpose (image recognition, behavior model identification, reasoning, …), whereas the human is able to manage all of these tasks.
The goal should be the development of high-level algorithms that combine different functions on a high level, to come closer to Artificial Intelligence.
Artificial ignorance: The 10 biggest AI failures of 2017!
Artificial intelligence (AI) frequently made tech headlines in 2017, often for innovative new products and growth in the workplace.
Multiple reports examined the technology's future and implications. An October Gartner reportpredicted that AI will create 2.3 million jobs by 2020. And China is now racing to AI world dominance, which may mean a global power shift.
Experts predicted AI's growth and potential moral issues for business users and consumers. While only 17% of developers ended up working with the technology in 2017, three-fourths of those that did not plan on using AI or machine learning in 2018, implying continued growth.
But, like most emerging fields, AI dealt with a few noteworthy growing pains.
Here are 10 blunders that left some wondering when AI will become intelligent...
https://www.techrepublic.com/article/the-10-biggest-ai-failures-of-2017/?ftag=TREabb1cd6&bhid=26515897512689358169344413145444
Question: What is AI's history ? Possible Answer --- Natural Philosophy? What is Natural Philosophy ? Answer --- Could it be Science ? What is AI? Possible Answer -- Natural Philosophy? Is this just a bunch of gobbly gook or is it a circle and we are all stuck?
Here are three limits to AI's use in the enterprise that tech and business leaders tend to overlook.
1. Data
2. Lack of knowledge
3. Bias
https://www.techrepublic.com/article/the-3-most-overlooked-limitations-of-ai-in-business/
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