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 Christmas approaching, online retailers are facing an onslaught of fraudsters, and machine learning could prove vital to pattern detection. Stripe has revealed insights from its data to help online businesses fight online fraud this Christmas, and has recommended that retailers add machine learning tools to their respective arsenals. Stripe examined transaction data from hundreds of thousands of its customers across 25 countries. 'We recommend using anti-fraud tools based on machine learning trained on large amounts of data, to ensure businesses are making the right trade-offs between battling fraud and maximising profits' – MICHAEL MANAPAT While chip-enabled credit cards have made bricks-and-mortar shopping safer, fraudsters are increasingly targeting online stores. However, unlike physical stores, online businesses are unfortunately responsible for paying the associated costs.
You've probably heard versions of each of the following ideas. With computers becoming remarkably adept at driving, understanding speech, and other tasks, more jobs could soon be automated than society is prepared to handle. This "superintelligence" will largely make human labor unnecessary. In fact, we'd better hope that machines don't eliminate us altogether, either accidentally or on purpose. Even though the first scenario is already under way, it won't necessarily lead to the second one.
There was no one home when Avisheh Madani arrived to tour a San Francisco rental property. No one human, that is. Madani, 35, used a code from an app to unlock the door and was greeted immediately by a robot. "It was definitely weird," she said. The robot, really a moveable video monitor, is the brainchild of Zenplace, a rental management company based in San Francisco and expanding quickly across the nation.
A person's university years should be all about expanding your horizons, as well as meeting people with perspectives and backgrounds different from your own. Well, what could be more different than sharing your classroom with a robot? That's what 31 philosophy students at Notre Dame de Namur University in California recently experienced when they were joined in their "Philosophy of Love" program by Bina48, an A.I. animatronic robot. The robot participated via Skype in a series of sessions before appearing "in person" in the final class. "I wasn't sure how the students would react, but they were psyched about it," Professor William Barry, an associate professor of philosophy at Notre Dame de Namur, told Digital Trends.
Artificial intelligence and machine learning have been game-changing solutions for enterprises recently, and they continue to be an important part of a successful strategy for IT leaders. It is important to understand that adopting machine learning for product development is becoming necessary -- and if a company wants to meet their goals, they will be lagging behind competitors in the coming decade if they don't adopt ML. So, it is necessary to get started with machine learning as soon as possible. If you're willing to achieve this objective, here are some tips to get started. Many organizations understand machine learning and data science, but they do not know how they should be implemented.
Back in the 1990s an intrepid group of researchers out of the University of Pittsburgh set out to write a computer program that could do a better job than doctors of predicting whether serious complications would develop in patients who presented with pneumonia.1 Success may have been a long shot, but it was definitely a shot worth taking. After all, the researchers figured that if they pulled it off, they could both lower costs and improve patient outcomes in one fell swoop. So they built a neural network -- basically a computer program that responds dynamically to external inputs -- and turned it loose on a database covering three-quarters of a million patients in 78 hospitals across 23 states. The results were curious, to say the least. The program seemed to have determined that patients with pneumonia and asthma had better outcomes than those who did not have asthma.
Artificial intelligence and machine learning, which found solid footing among the hyperscalers and is now expanding into the HPC community, are at the top of the list of new technologies that enterprises want to embrace for all kinds of reasons. But it all boils down to the same problem: Sorting through the increasing amounts of data coming into their environments and finding patterns that will help them to run their businesses more efficiently, to make better businesses decisions, and ultimately to make more money. Enterprises are increasingly experimenting with the various frameworks and tools that are on the market and available as open source software, in both small scale experiments run by a growing number of data scientists who have the expertise to find the valuable information the growing lakes of data and in full blown production deployments that are, conceptually, every bit as sophisticated as what the hyperscalers are deploying. The top cloud service providers and hyperscalers have for several years embrace data-driven AI and machine learning techniques and built their own internal frameworks and platforms that enable them to quickly take advantage of them. But as the technologies begin to cascade into more mainstream enterprises, the complexity of software and systems are throwing roadblocks in front of initiatives aimed at leveraging AI and machine learning for the good of the business.
Machine learning enables computers to learn from large amounts of data without being explicitly programmed to do so. We can already see how machine learning gives rise to new intelligent applications, from self-driving cars to intelligent assistants on our smartphones. Increasingly, businesses recognize the importance of using machine learning to transform their data assets into business value. However, many companies are unsure how machine learning can be applied to solve problems in an enterprise context. As the world's most relevant enterprise data is part of SAP's system and business network, SAP aspires to make all its enterprise solutions intelligent and help customers to leverage their data.
Thursday afternoon, NASA announced a new discovery from its Kepler space telescope mission – the presence of two new exoplanets. While that alone isn't that remarkable, since Kepler has discovered and confirmed upwards of 2,300 exoplanets in distant solar systems, how scientists found those planets is noteworthy. Andrew Vanderburg, astronomer and NASA Sagan Postdoctoral Fellow at the University of Texas at Austin, who worked on this discovery, says the planet was discovered using artificial intelligence. "Instead of using traditional methods to identify exoplanets, we've incorporated an advanced type of machine learning called a neural network to help identify planets and sort away the bad signals, the false positive signals from the real planets," he says. "It's a form of artificial intelligence, very loosely inspired by the structure of neurons in your brain."
The increased use of artificial intelligence and machine learning is shifting the paradigm of medical research and treatment. These advanced technologies are providing researchers real-time access to every white paper and clinical case study conducted on a genetic disorder. Being able to develop such an elaborate database of information allows researchers to not only understand the full scope of a medical condition, but further shorten the amount of time it takes to develop a cure. Founded in 2011 by Gunjan Bhardwaj and Guarav Tripathi, Innoplexus is a technology and product development company focused on solving complex challenges in the pharmaceutical and life sciences industries. Their end-to-end platform for Life Sciences research uses artificial intelligence to generate smart data and insights to assist in the discovery, clinical development and regulatory compliance of pharmaceutical medicine.