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) …
Artificial Intelligence (AI) platforms enable organizations and individuals to build intelligent applications based on data. AI platforms must provide the facilities to ingest complex data, address rapidly generated or constantly evolving data, rectify and amplify hard-to-find signals, craft models where human-powered analytics are slow, enable resolution for highly iterative models, decrease the time to generate models, and improve accuracy rates. Seven key components for AI success include the ability to handle large amounts of complex data, deliver massive compute power, compress time, provide math talent, embody domain expertise, leverage human user experience, and support contextual decisions. Best-of-breed platforms do not rely on one cloud vendor to handle the complex data, massive compute power, and time compression. Instead, they reserve the option to apply a multi-cloud strategy.
The basic idea behind regression analysis is to take a set of data and use that data to make predictions. A useful first step is to make a scatter plot to see the rough shape of your data. Then, choose a regression method to find the line of best fit. Which method you choose depends upon the shape the scatter plot reveals (is it a line, a curve, or a parabola?) The following image shows an overview of regression; See below for links to more detail.
In my software development work, I've spent considerable time figuring out how to integrate other tools and technology to make my solutions better. The last few years, my work has expanded to adding machine learning technology into various software and app solutions. Since it's been something new for myself and my developer team, we've learned a significant number of new things about how it works and the best way to integrate this segment of artificial intelligence (AI) into the tools and platforms we are building. Before I share some of the best practices our developer team has created based on experiences using machine learning, I'd like to give you some reasons to move forward with this technology as part of what you may be developing as a tech entrepreneur. Machine learning uses real-time data to analyze existing information in order to predict a future action and direct the response.
Bill Stasior, the former head of Apple's Siri division, is leaving the company after nearly a decade to join Microsoft's artificial intelligence division, reports The Information. Although Stasior left Apple in May, he's only joining Microsoft later this month as a corporate vice president, reporting to Chief Technology Officer Kevin Scott. Stassior worked at Apple for more than seven years, joining back in 2012. Stasior's departure seems less an indictment of the current state of Siri and more a reflection of the reality of AI at Apple. Last year, the iPhone maker poached John Giannandrea from Google, where he was a former head of search and AI.
Huawei said on Friday it expects to lose $10 billion (£8bn) in revenues this year to sanctions imposed by the US in May, lower than an earlier estimate of $30bn. The company made the remarks as it unveiled a new AI chip and computing framework as part of broader efforts to phase out its reliance on technology made in the US. Huawei deputy chairman Eric Xu said the company was doing "much better" than initially feared, but that a sales "reduction of more than $10bn could happen". The company's revenues gained a boost from domestic sales, which surged by nearly one-third year-on-year in the June quarter. In May the US placed Huawei on a national security "entity list" that prevents US firms from trading with it, and while it has thus far imposed a series of delays that have prevented the sanctions from taking place, the uncertainty has led to a steep drop in Huawei's global sales.
Google has developed software that could pave the way for smartphones to interpret sign language. The tech firm has not made a product of its own but has published algorithms which it hopes developers will use to make their own apps. Until now, this type of software has only worked on PCs. But campaigners from the hearing-impaired community have welcomed the move, but say the tech might struggle to fully grasp some conversations. In an AI blog, Google research engineers Valentin Bazarevsky and Fan Zhang said the intention of the freely published hand-tracking technology - which can perceive the shape and motion of hands - was to serve as "the basis for sign language understanding".
In his writings, Gary Marcus is clear about two things: Artificial intelligence is an extremely promising technology that, if used in the right way, could significantly improve practices in health care and other industries. But right now, Marcus says, AI is getting off track, with potentially severe consequences for society and the field itself. That viewpoint makes Marcus -- a tech entrepreneur, author, and psychology professor at New York University -- a controversial figure in the world of artificial intelligence. He is among a few prominent scientists voicing skepticism about the dominance of deep learning, a type of AI architecture whose use has exploded in medicine and other fields. Unlock this article by subscribing to STAT Plus and enjoy your first 30 days free!
For a long, long time, renewable energy proponents have considered advancements in battery technology to be the Holy Grail of the industry. Advancements in energy storage has been among the hardest to achieve economically, thanks to the incredibly tricky chemistry that's involved in storing power. Now, one company that's launching from Y Combinator believes it has found the key to making batteries better. The company is called Holy Grail and it's launching in the accelerator's latest cohort. With an executive team that initially included Nuno Pereira, David Pervan and Martin Hansen, Holy Grail is trying to bring the techniques of the fabless semiconductor industry to the world of batteries.
Combining a digital twin with artificial intelligence (AI) can remove much of the guesswork and expense that comes with manufacturing a product. But what exactly is a digital twin, or virtual replica, and how does it streamline your production process in the real world? Dr. Norbert Gaus of Siemens Corporate Technology defines the concept of a digital twin as a "digital representation of a physical product in all its aspects." Digital twin technology can speed time to market, reduce costs, and allow a company to create a much broader portfolio of products. Dr. Gaus explains how AI-based simulations can take the place of creating multiple physical prototypes to achieve new designs. He describes how Siemens combines a digitized version of the physical product with artificial intelligence throughout the product life cycle. That product lifecycle includes design, components, manufacturing, operations, and service and maintenance. In this video, Dr. Gaus also discusses the challenges that Siemens has faced in the last ten years bringing digital twin automation to life.