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) …
With the number of connected devices set to top 11 billion – and that's not including computers and phones – in 2018, Internet of Things will clearly continue to be a hot topic. I had the chance to speak to Bret Greenstein, VP of IBM's Watson IoT Consumer Business, who highlighted four key trends. Interestingly three of those trends were around convergence with other distinct yet highly correlated technologies. This underlines the principle that data is the fundamental ingredient of digital transformation. The technologies predicted to make big waves in the coming year – including IoT, artificial intelligence, blockchain and edge – are all methods of collecting, analyzing and storing information.
Amazon is every online retailer's forbidding nightmare. Last year, it dominated 44 percent of the US eCommerce market and about 4 percent of all domestic retail sales. One Click Retail, an eCommerce analysis provider, explains its dominance with the fact that millennials, Amazon's core demographic, are getting older and starting to spend more. Moreover, advanced marketing capabilities for sellers, developments in Alexa, and pioneering in applications of the hottest technologies make it impossibly hard for smaller competitors to actually… well, compete. Amazon is not only a simple and familiar platform selling everything you can think of – it's also one of the most innovative players on the market.
Human Factors in Hypertext (HUMAN) Opinion Mining, Summarization and Diversification Narrative and Hypertext I attended the Opinion Mining, Summarization and Diversification workshop. The workshop started with a talk titled: "On Reviews, Ratings and Collaborative Filtering," presented by Dr. Oren Sar Shalom, principal data scientist at Intuit, Israel. Next, Ophélie Fraisier, a PhD student studying stance analysis on social media at Paul Sabatier University, France, presented: "Politics on Twitter: A Panorama," in which she surveyed methods of analyzing tweets to study and detect polarization and stances, as well as election prediction and political engagement. He showed how collective opinion mining can help capture the drivers behind opinions as opposed to individual opinion mining (or sentiment) which identifies single individual attitudes toward an item. I thank a million people! https://t.co/I3quPp6nw3 He also discussed a phenomenon in which people are likely to lie to pollsters (social desirability bias) but are honest to Google ("Digital Truth Serum") because Google incentivizes telling the truth. The paper sessions followed the keynote with two full papers and a short paper presentation. Google search data as "digital truth serum" - while reporting of child abuse go down at the recession time, Google search data indicates that real child abuse increases https://t.co/DQQoAotZqB However, it feels more like a research talk rather than a #keynote.
Machine Learning Apps are fast invading into our everyday lives as the technology is progressing towards delivering smarter mobile-centric solutions. Embedding mobile apps with Machine Learning, a promising segment of AI, is spelling out a lot of advantages for the adopting companies to stand out amidst the clutter and rake in sizeable profits. Many organizations are investing heavily in Machine Learning to reap its benefits. Based on a prediction, Machine Learning as a service market will touch $5,537 million by 2023 while growing at a CAGR of 39 per cent from 2017-2023. Machine Learning Applications refer to a set of apps with Artificial Intelligence mechanisms that are designed to create a universal approach throughout the web to solve similar problems.
When you go to the movies, how do you decide what you want to see? Maybe you're more likely to purchase a ticket if a movie is part of an established franchise in which you are already invested. Maybe a beloved actor or the buzz of awards-season brings you to the big screen. Or maybe a friend hasn't stopped raving about a recent release and you just have to check it out for yourself. Whichever reason brings you to the movies, the question has now become whether artificial intelligence (AI) can predict what you're most likely to see.
The hype of Artificial Intelligence is towering to reach the global market value of approximately 7,35 billion US dollars in 2018 and expected to generate 89,85 billion US dollars in 2025. The penetration of this cognitive function has almost knuckled every industry. However, AI is known narrow in executing the tasks that don't require insightful research. The healthcare's adoption of Artificial Intelligence is expected to outstretch all other relevant industries. Healthcare Industry is one of the largest industry with massive patient's database.
Customer churn is a major headache for most companies and threatens to put the brakes on the red-hot growth of the pay-as-you-go (PAYGo) solar sector. With over 1 million units sold in the last 5 years and over 50,000 units installed each month, the PAYGo model makes solar affordable for end-users and provides sufficient margin for providers to scale last-mile distribution. However, for the model to succeed PAYGo operators must retain customers and build a base of loyal and engaged customers. Our project with Zola Electric (formerly Off Grid Electric) demonstrates that machine learning can help them do so. PAYGo operators make money from installments and/or fees as end-consumers pay off solar assets over 1 to 3 years.
It wasn't that long ago that Deepmind's AlphaGo proved it could play the game better than the best humans. From the standpoint of the range of possible future moves, the game of Go is not a searchable problem. It represents search spaces that are astronomically larger than all the potential moves in chess. Yet, the individual moves are far simpler and more atomic than chess (and almost any other game) partly because of the incredible simplicity of the rules combined with a giant catalog of hundreds of thousands of human played games. Because it was relatively easy to have it play a large number (countless millions) of games against itself, the game is a good fit for deep learning.
Machine learning is a method used to make complex models and algorithms by analysing huge amount of data, that lend themselves to prediction, making use of computers. It has strong relation with mathematics. Which optimizes and delivers methods, theory and application domains to this field. It is sometimes conflated with data mining, whereas Data Mining is process where intelligent methods are applied to extract data patterns. Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms.