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) …
While technological advances say they are on the brink of achieving that perfect artificial intelligence, we are not quite there yet. Fortunately for us, an AI does not need to be irreproachable, just better than a human. Take connected cars, for instance. An AI-based driver may not be mistake-proof, but it is certainly less imperfect than a human driver. This is very much the case in cybersecurity where IT experts are changing the rules of the game using Machine Learning.
TGI Friday's may have a reputation as a casual restaurant and watering hole, but its messaging to customers was hardly conversational. The well-known chain sent out regular blasts through traditional broad-reach media and, more recently, social media, yet it increasingly wanted to re-create the banter that happens organically when regulars belly up to the bar. In lieu of hiring a battalion of customer service "bar keeps," TGI Fridays recruited an enterprise conversation platform infused with a shot of machine learning and artificial intelligence (AI) to personalize its messaging and overall customer experience. Now, patrons can chat up the AI for happy hour suggestions and appetizer specials, engage in small talk using emojis, make reservations, and order takeout via social media channels and through Amazon Alexa. "We thought about how technology could help us create that one-on-one personalized messaging outside of the bar without having to hire 1,000 people to respond to individual guests," says Sherif Mityas, vice president of strategy and brand initiatives, as well as acting CIO, at TGI Fridays.
There's currently a shortage of over seven million physicians, nurses and other health workers worldwide, and the gap is widening. Doctors are stretched thin -- especially in underserved areas -- to respond to the growing needs of the population. Meanwhile, training physicians and health workers is historically an arduous process that requires years of education and experience. Fortunately, artificial intelligence can help the healthcare sector to overcome present and future challenges. Here's how AI algorithms and software are improving the quality and availability of healthcare services.
Google has revealed details about how its custom Tensor Processing Unit speeds up machine learning; here's how the field is set to evolve in its wake Google is nothing if not ambitious about its machine learning plans. Around this time last year it unveiled its custom Tensor Processing Unit (TPU) hardware accelerator designed to run its TensorFlow machine learning framework at world-beating speeds. Now, the company is providing details of exactly how much juice a TPU can provide for machine learning, courtesy of a paper that delves into the technical aspects. The info shows how Google's approach will influence future development of machine learning powered by custom silicon. Machine learning generally happens in a few phases.
As government agencies are beginning to turn over security to automated systems that can teach themselves, the idea that hackers can sneakily influence those systems is becoming the latest (and perhaps the greatest) new concern for cybersecurity professionals. Adversarial machine learning is a research field that "lies at the intersection of machine learning and computer security," according to Wikipedia. "It aims to enable the safe adoption of machine-learning techniques in adversarial settings like spam filtering, malware detection and biometric recognition." According to Nicolas Papernot, Google PhD Fellow in Security at Pennsylvania State University, AML seeks to better understand the behavior of machine-learning algorithms once they are deployed in adversarial settings -- that is, "any setting where the adversary has an incentive, may it be financial or of some other nature, to force the machine-learning algorithms to misbehave." "Unfortunately, current machine-learning models have a large attack surface as they were designed and trained to have good average performance, but not necessarily worst-case performance, which is typically what is sought after from a security perspective," Papernot said.
Becoming a cloud-centric technology company is a given nowadays for companies that consider themselves future-ready. The question is hence, not whether a company is operating in the cloud, but what level of sophistication they have reached in their cloud endeavors. This is because the cloud is being enriched by incorporating other emerging technologies, especially machine learning. There is no doubt that contemporary cloud networks will be more intelligent than ever. And companies must harness the power of the intelligent cloud to realize value.
Artificial intelligence has reached peak hype. News outlets report that companies have replaced workers with IBM Watson and that algorithms are beating doctors at diagnoses. New AI startups pop up everyday, claiming to solve all your personal and business problems with machine learning. Ordinary objects like juicers and Wi-Fi routers suddenly advertise themselves as "powered by AI." Not only can smart standing desks remember your height settings, they can also order you lunch.
The latest technology now is artificial intelligence. The importance of this technology cannot be over emphasized. This is why app developers usually make use of it in many app development projects. To underscore the usefulness of artificial intelligence, some of its applications have been outlined below. The industry that first made use of artificial intelligence is the video game industry.
Many companies today are employing deep learning techniques in different facets of their business. Yelp uses deep learning algorithms to feature the best user photos, Netflix uses it to suggest movies you might be interested in, and Google ultimately transformed the concept of deep learning by creating a system that helps generate responses to search queries. It's widely believed that you no longer need to know your data; you can just apply a little deep learning magic and poof -- problem solved. However, the reality is that this could not be further from the truth, at least for the legal industry. As an example, deep learning can be essential when legal counsel within an organization wants to find out how many contracts (among 10s to 100s of thousands) has termination for convenience clauses that could disrupt the business, or if any have strict assignment rules that may be a problem for a M&A event.
The relentless drive to digital transformation among tech and non-tech companies pushed mergers and acquisitions to record levels over the past year, the latest analysis finds. Now, artificial intelligence and machine learning loom as the next wave of hot targets for companies seeking new market leverage. EY, which tracks M&A activity quarter-by-quarter and year-by-year, puts aggregate 2016 deal value at $466.6 billion, exceeding 2015 levels by two percent -- and establishing the highest level ever recorded. However, the actual volume of M&A deals has colled somewhat, to 3,076 deals, a five-percent decline. At the core of this deal-making frenzy is interest in the Internet of Things (IoT), in which deal value tripled to $103.4 billion through 2016.