Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Artificial intelligence (AI) has been steadily influencing business processes, automating repetitive and mundane tasks even for complex industries like construction and medicine. While AI applications often work beneath the surface, AI-based content generators are front and center as businesses try to keep up with the increased demand for original content. However, creating content takes time, and producing high-quality material regularly can be difficult.

Artificial intelligence has made significant strides in recent years, but modern AI techniques remain limited, a panel of MIT professors and the director of the MIT-IBM Watson AI Lab said during a webinar this week. Neural networks can perform specific, well-defined tasks but they struggle in real-world situations that go beyond pattern recognition and present obstacles like limited data, reliance on self-training, and answering questions like "why" and "how" versus "what," the panel said. The future of AI depends on enabling AI systems to do something once considered impossible: Learn by demonstrating flexibility, some semblance of reasoning, and/or by transferring knowledge from one set of tasks to another, the group said. The panel discussion was moderated by David Schubmehl, a research director at IDC, and it began with a question he posed asking about the current limitations of AI and machine learning. "The striking success right now in particular, in machine learning, is in problems that require interpretation of signals--images, speech and language," said panelist Leslie Kaelbling, a computer science and engineering professor at MIT.

A new training program from AI and auto machine learning vendor DataRobot aims to teach citizen data scientists, including business analysts and data analysts, practical data science and AI skills. The paid program, "10x: The Applied Data Science Academy," provides instruction in skills such as problem framing, exploring data, feature engineering, and deploying models. It involves 40 hours of hands-on, self-paced training, 20 hours of practical labs, and 40 hours on a capstone project. With the program, revealed on Wednesday during DataRobot's AI Experience Worldwide virtual conference, held June 16-17, DataRobot enters an already crowded online AI training field. Vendors such as Google, IBM, and Microsoft have long offered free and paid analytics and AI training programs, as have many colleges and universities.

Retail will lead the way in spending on artificial intelligence systems, according to IDC, investing $5.9 billion worldwide this year in chatbots, automated purchase advisors and product recommendations. Overall spending on AI systems is projected to reach $35.8 billion in 2019, up 44 percent over last year, and will more than double to $79.2 billion in 2022. The U.S. will deliver nearly two-thirds of that spending. The AI use cases that will see the most investment this year are automated customer service agents ($4.5 billion), sales process recommendation and automation ($2.7 billion), and automated threat intelligence and prevention systems ($2.7 billion), said IDC. "Significant worldwide artificial intelligence systems spend can now be seen within every industry as AI initiatives continue to optimize operations, transform the customer experience, and create new products and services," said IDC's Marianne Daquila, research manager, customer insights and analysis. "IDC is seeing that spending on both AI software platforms, and AI applications are continuing to trend upwards and the types and varieties of use cases are also expanding," said David Schubmehl, research director, cognitive/artificial intelligence systems.

In the wake of Amazon's hiring debacle involving an artificial intelligence (AI)-powered recruiter discriminating against women, experts maintain that machines themselves aren't biased -- they're only mirroring and amplifying cultural biases in the real world. The solution to shoddy machine learning, these experts say, is deceptively simple: better data. Worldwide spending on cognitive and artificial intelligence (AI) systems reached an estimated $19 billion, almost doubling the amount from 2017, according to research firm IDC. The promise of AI technology has spawned a race between the U.S. and China, both fighting for the title of global leader. According to a report by PwC, the industry is expected to boost U.S. GDP by 14.5% by 2030 -- second only to China, which is expected to see a 26.1% boost.

Remember Tay, Microsoft's experimental AI chatbot that unleashed racist commentary after learning through interaction with its Twitter followers? Then there was COMPAS, AI-based software used by law enforcement to assess the risk of recidivism in offenders, which was found to be biased against people of color. More recently, Amazon secretly shelved a recruiting tool that was shown to unfairly discriminate against potential female would-be hires. These high-profile examples illustrate both the potential and peril the artificial intelligence (AI) revolution presents. Software developers are already tapping AI algorithms to facilitate loan approvals in the banking industry, to improve diagnostic decisions in emergency rooms, and to streamline the hiring process.

Jun 6, 2016 @ 06:00 AM AI Is The Wave, And CIOs Must Learn To Surf Share to email Oracle Tweet This As compute power increases, so will the capabilities of machine-learning models. " As compute power increases, so will the capabilities of machine-learning models. After years percolating in the backwaters of IT, AI is catching a dynamic wave of interest and investment. Self-driving cars and Go-playing computers have grabbed the public's attention. But AI's potential to dramatically improve cost/benefit equations is why "CIOs should start looking at how this is going to change the business they're in and potentially disrupt the types of applications they're using," says IDC Research Director David Schubmehl. Source: iStockphoto If robotics is the face of AI, the beating heart is what's known as machine learning--the ability to program a computer to recognize patterns and build models that let it make decisions or generate predictions. Several factors account for the recent explosion in ...

Data is the new oil, and an enterprise IoT deployment is an easy way to get a lot of it from many different sources. But in the end, it's what a business does with its data that really matters. By putting that data to work, organizations can improve efficiency, boost the bottom line, and drive innovation. This ebook, based on the latest ZDNet/TechRepublic special feature, explores ways IoT is improving operations and delivering business value to enterprises around the world. That's where machine learning comes in.

Artificial intelligence (AI) has captured the human imagination for millennia. According to Greek myth, Hephaestus, blacksmith of the gods, forged the gigantic bronze automaton Talos to safeguard the isle of Crete. Talos was animated with ichor, an ethereal fluid that flowed like blood in the gods' veins. The metal colossus hunted pirates and invaders with abandon, hurling massive boulders to fend off unwelcome ships while circling Crete three times per day. Most significantly, this self-operating machine was fully capable of independent thought and action -- the first artificially intelligent construct of ancient legend.

An image of a dog created by a deep convolutional generative adversarial network (GAN) algorithm. Back in 1999, Hany Farid was finishing his postdoctoral work at the Massachusetts Institute of Technology (MIT) and was in a library when he stumbled on a book called The Federal Rules of Evidence. The book caught his eye, and Farid opened to a random page, on which was a section entitled "Introducing Photos into a Court of Law as Evidence." Since he was interested in photography, Farid wondered what those rules were. While Farid was not surprised to learn that a 35mm negative is considered admissible as evidence, he was surprised when he read that then-new digital media would be treated the same way.