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) …
The race to become the global leader in artificial intelligence (AI) has officially begun. In the past fifteen months, Canada, China, Denmark, the EU Commission, Finland, France, India, Italy, Japan, Mexico, the Nordic-Baltic region, Singapore, South Korea, Sweden, Taiwan, the UAE, and the UK have all released strategies to promote the use and development of AI. No two strategies are alike, with each focusing on different aspects of AI policy: scientific research, talent development, skills and education, public and private sector adoption, ethics and inclusion, standards and regulations, and data and digital infrastructure. This article summarizes the key policies and goals of each strategy, as well as related policies and initiatives that have announced since the release of the initial strategies. It also includes countries that have announced their intention to develop a strategy or have related AI policies in place. I plan to continuously update this article as new strategies and initiatives are announced. If a country or policy is missing (or if something in the summary is incorrect), please leave a comment and I will update the article as soon as possible. I also plan to write an article for each country that provides an in-depth look at AI policy. Once these articles are written, I will include a link to the bottom of each country's summary. June 28: Publication of original article, included Australia, Canada, China, Denmark, EU Commission, Finland, France, Germany, India, Japan, Singapore, South Korea, UAE, US, and UK.
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.
Olay, the popular skin care brand, started using AI to make recommendations to its millions of users almost two years ago, and says it has doubled the company's sales conversion rate overall. It's just the latest retail company that has turned to AI to boost its engagement with users to increase its top line. The traction confirms surveys that show an increasing number of businesses are putting AI investments at the head of their agenda. True, Olay has an advantage over most companies. The billion-dollar brand is owned by giant Procter & Gamble, and has been using AI in its core product for some time.
AUSTIN, TX--As indicators prove the economy is getting stronger and consumers place a greater emphasis on experiences rather than products, there's a demand for travel. "In addition to strong economic variables and confident consumers with additional disposable incomes, consumer expectations play a major role in this summer's record-breaking travel season," said Noreen Henry, CEO of WayBlazer, a B2B travel technology company. The hotel industry is adopting advancements in technology that offer travelers convenience, ease of travel and digital access to the experiences they crave. "As brands within the travel space begin to adopt new technology, consumers are becoming much more likely to book trips. Consumers want convenience and personalization, and brands are meeting these demands through innovation," said Henry.
The relevance of search results and product recommendations should depend on a visitor's intent, not the accuracy of their words. Therefore, it shouldn't matter whether a visitor uses the words cookies or biscuits to describe what they are looking for – the products displayed to them in search and recommendations should be sweet baked treats regardless. It's by understanding how products relate to one another that a web page can effectively show visitors the full range of relevant products that match their buying intent.
The use of machine learning in finance can do wonders, even though there is no magic involved. Successful machine learning projects often depend on choosing the right datasets and applying the right algorithms. Let's take a closer look at why this technology is a great fit for finance, what implementations it has in that domain, and how financial services companies can utilise it. Machine learning is a subset of Data Science. While Data Science covers the whole data processing pipeline, Machine Learning is about using specific algorithms and chosen datasets to train mathematical models to find patterns, make predictions, segmentation, and more.
Entertainment companies are entering the Age of Data, where they'll have access to more information than ever about their products, their audiences and how to create, market and distribute one to the other. Now, those companies and their leadership have to be ready to embrace the coming huge opportunities, especially as data-driven competitors such as Netflix, MoviePass and Amazon transform the industry. That was one message this morning from Stephen F. DeAngelis, CEO and founder of AI provider Enterra Solutions, speaking before a group of Hollywood technology executives in Beverly Hills. He noted wryly that Hollywood has portrayed AI technologies in dark or at least complicated ways over the years, from the murderous HAL 9000 in 2001: A Space Odyssey to the world-ending SkyNet in the Terminator films to the runaway AIs of Ex Machina and Her. We're quite a ways still from AI with that kind of power and autonomy, DeAngelis said, but he cautioned that people think of AI tools in overly limited ways.
Every minute of every hour we hear more about the vast, sometimes scary power of artificial intelligence. We've invited it into our homes and businesses. In some cases, it has come in uninvited. Last week, the President of Microsoft, Brad Smith, wrote a piece about the need for both regulation and corporate responsibility to ensure that technology is used for good. And technology can do incredible good.
In honor of Amazon Prime Day, let's take a look at the inner workings of this company that is pushing the bounds of innovation, not only with Amazon Prime, but the many other cutting-edge management strategies. The company that sets the tone for so many aspects of customer experience is breaking down internal barriers and showing how other companies can do the same. Amazon, a leader in customer experience innovation, has taken things to the next level by reorganizing the company around its AI and machine learning efforts. Amazon's approach to AI is called a flywheel. In engineering terms, a flywheel is a deceptively simple tool designed to efficiently store rotational energy.
In the broad sweep of AI's current worldly ambitions, machine learning healthcare applications seem to top the list for funding and press in the last three years. Since early 2013, IBM's Watson has been used in the medical field, and after winning an astounding series of games against with world's best living Go player, Google DeepMind's team decided to throw their weight behind the medical opportunities of their technologies as well. Many of the machine learning (ML) industry's hottest young startups are knuckling down significant portions of their efforts to healthcare, including Nervanasys(recently acquired by Intel), Ayasdi (raised $94MM as of 02/16), Sentient.ai With all the excitement in the investor and research communities, we at TechEmergence have found most machine learning executives have a hard time putting a finger on where machine learning is making its mark on healthcare today. We've written this article, not to be a complete catalogue of possible applications, but to highlight a number of current and future uses of machine learning in the medical field, with relevant links to external sources and related TechEmergence interviews.