If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
"I think a very good precedent that we have in recent history is the web," says François Chollet, a prominent AI researcher and author of Keras, an open source neural networks library. But with all of this grand talk about how AI is changing us and our world, it's easy to forget something more important: how we're changing AI. This technology is the sum of choices made by individual researchers and engineers each day. "Open source has become the default for AI," Chollet says.
Intimate computing is when it knows you. While today's sales assistant is a device, such as a smartphone or a laptop, within 10 years it will become something you can talk to, similar to the Amazon Echo. It will be as if you have someone whispering real-time sales intelligence in your ear. That's why my preferred term is not artificial intelligence but augmented intelligence, because this technology actually makes the human salesperson much more capable by augmenting them.
Fewer technologies are hotter than artificial intelligence (AI) and machine learning (ML), which mimic the behavior of the human mind to help companies improve business operations. Even Uber, weathering several legal challenges, has made time to reveal Michelangelo, an internal ML-as-a-service platform, that "democratizes machine learning and makes scaling AI to meet the needs of business as easy as requesting a ride." For the past several months, he has been using Salesforce.com's Einstein AI/ML technology to increase personalization across the bank's small business, wholesale, commercial wealth and commercial banking units. Key advice: Using ML to identify patterns is the key to creating self-healing capabilities.
In today's day and time while most organizations are busy revamping their Policy administration systems which were long ready to be replaced a decade ago, what will set companies apart will be the organizations that start considering Machine Learning and Artificial intelligence(AI) for their core systems. In every type of insurance product the claims experience influencing the pricing and risk aggregation decision making done by the insurer. If the dots are connected and the data patterns understood and logic applied there are certain decision making aspects that can move away from people to machines and over time evolve to largely autonomous ecosystem. So before we set the drones to fly and change the commercial insurance ecosystem, Machine learning and AI need to be adopted into mainstream core software platforms.
While having myself a strong mathematical background, I have developed an entire data science and machine learning framework (mostly for data science automation) that is almost free of mathematics, and known as deep data science. You will see that you can learn serious statistical concepts (including limit theorems) without knowing mathematics, much less probabilities or random variables. Anyway, for algorithms processing large volume of data in nearly real-time, computational complexity is still very important: read my article about how bad so many modern algorithms are and could benefit from some lifting, with faster processing time allowing to take into account more metrics, more data, and more complicated metrics, to provide better results. It looks like f(n), as n tends to infinity, is infinitely smaller than log n, log(log n), log(log(log n))), and so on, no matter how many (finite number of) nested log's you have.
The forecast is not all gloomy – artificial intelligence (AI), machine learning (ML) and automation are also expected to create jobs that will likely be much more interesting and creative than the repetitive tasks of the industrial age. According to Andrew McAfee, principal research scientist at MIT and co-director of the university's Initiative on the Digital Economy (IDE), AI amounts to, "the largest disruption in labor and the way we work," in generations. But as Joi Ito, director of the MIT Media Lab and moderator of a panel titled, "Putting AI to Work," put it, the fear that machines will become smarter than humans and take over the world is tempered by the reality that "they're stupid and they've already taken over the world." Seth Earley, CEO of Earley Information Science, while agreeing there will be, "an enormous amount of disruption," from AI, was more optimistic about retraining for the jobs of the future.
Jeanne Ross is principal research scientist for MIT's Center for Information Systems Research. Because, as with enterprise systems, AI inserted into businesses drives value by improving processes through automation. An AI application might allow financial analysts to spend less time extracting data on financial performance, but it adds value only if someone spends more time considering the implications of that performance. Jeanne Ross is principal research scientist for MIT's Center for Information Systems Research.
Perhaps it's not a coincidence that just this month, Box announced a partnership with Google to bring AI via image recognition technology to the cloud content management firm. Last week, M-Files, a hybrid content management solution, announced it was acquiring Apprento, a Canadian startup that uses natural language processing (NLP) and natural language understanding (NLU) to provide semantically based intelligent summaries. "In Apprento's case, we were first attracted to their practical experience with applying natural language processing (NLP) and natural language understanding (NLU) to practical business needs. All of these moves suggest that we could be in the midst of an industry shift that Levie and Patel alluded to, as content management firms try to use intelligence to make sense of the increasingly large amount of content moving into the enterprise.
Watson powers the company's chatbot GWYN (Gifts When You Need) and helps it detect user tone. GWYN interacts with online customers using natural language and is designed to understand human intention behind each purchase by interpreting and asking several questions. Last year, an AI teaching assistant powered by IBM Watson helped moderate an online forum for a computer science class at Georgia Tech University, and most students didn't find out they were interacting with AI. IBM Watson did a pilot with the Australian government around Nadia, a virtual assistant platform that helps disabled people get information about government services.
It is a machine-learning library using data flow graphs to build models. TensorFlow has been created for Deep Learning to let a user create a neural network architecture by himself (or herself, of course). Actually, tensors flow in the graph from node to node, thus making the name of the library sound logical. For some of you it may be interesting if there is any difference between TensorFlow and libraries like Theano, which also can make their own Deep Learning with multi-dimensional arrays and GPU.