Supply chain professionals are optimistic about the potential for artificial intelligence within their operations, but they have also struggled with the technology during the coronavirus pandemic, according to a survey from Secondmind, which develops machine-learning applications for businesses. The survey (which polled more than 500 supply chain managers and planners using AI) found that 90% of respondents believe AI will transform supply chains for the better by 2025, while 82% have been frustrated by AI-powered decisions during the course of the pandemic. The discrepancy highlights potential barriers of AI while underscoring that professionals who have experienced these issues still see a future for the technology. AI is an umbrella term that can include many statistical or computer science techniques. Gary Brotman, vice president of product and marketing at Secondmind, said he views AI as a term for processes that allow a computer to do something that would traditionally be done by a person.

Last week, Qualcomm announced the Snapdragon 845, which sends AI tasks to the most suitable cores. There's not a lot of difference between the three company's approaches -- it ultimately boils down to the level of access each company offers to developers and how much power each setup consumes. Before we get into that though, let's figure out if an AI chip is really all that different from existing CPUs. A term you'll hear a lot in the industry with reference to AI lately is "heterogeneous computing." It refers to systems that use multiple types of processors, each with specialized functions, to gain performance or save energy.

One of the primary benefits of AI today, whether you develop AI software, shoot video, or take photos, is that AI can accelerate the process and bring your product to reality quicker. Specifically, for software developers, the AI tools at their disposal for edge devices like smartphones are opening the way for new cutting-edge features and applications. "As a developer or as a creative, would you rather spend your time on the mundane challenges of programming or getting right to the creative side?" says Gary Brotman, senior director and head of AI strategy and product planning at Qualcomm Technologies. "AI, using neural networks, which means today's smartphone features produce photos as good as those you'd expect from a high-end DSLR camera." Plus, as technology evolves, all those familiar features like scene recognition, night mode photography, super resolution, and more can be applied in real time rather than during post-processing as they are today.

Tech's biggest players have fully embraced the AI revolution. Apple, Qualcomm and Huawei have made mobile chipsets that are designed to better tackle machine-learning tasks, each with a slightly different approach. Huawei launched its Kirin 970 at IFA this year, calling it the first chipset with a dedicated neural processing unit (NPU). Then, Apple unveiled the A11 Bionic chip, which powers the iPhone 8, 8 Plus and X. The A11 Bionic features a neural engine that the company says is "purpose-built for machine-learning," among other things.

It's hard to deny the impact of artificial intelligence (AI) in the enterprise. According to a 2017 study by Cowen, 81 percent of IT leaders are investing in or planning to invest in AI. Specifically, 43 percent are evaluating and doing a proof of concept (POC); 38 percent have already launched init...

It's hard to deny the impact of artificial intelligence (AI) in the enterprise. According to a 2017 study by Cowen, 81 percent of IT leaders are investing in or planning to invest in AI. Specifically, 43 percent are evaluating and doing a proof of concept (POC); 38 percent have already launched initiatives and are targeting more investments. With numbers like taht, it's no surprise that AI has also made its impact on the business side. It's crept into actual job titles in the enterprise, even if actual talent in developing AI may be hard to find.

Last week, Qualcomm announced the Snapdragon 845, which sends AI tasks to the most suitable cores. There's not a lot of difference between the three company's approaches -- it ultimately boils down to the level of access each company offers to developers, and how much power each setup consumes. Before we get into that though, let's figure out if an AI chip is really all that much different from existing CPUs. A term you'll hear a lot in the industry with reference to AI lately is "heterogeneous computing." It refers to systems that use multiple types of processors, each with specialized functions, to gain performance or save energy.

Tech's biggest players have fully embraced the AI revolution. Apple, Qualcomm and Huawei have made mobile chipsets that are designed to better tackle machine learning tasks, each with a slightly different approach. Huawei launched its Kirin 970 at IFA this year, calling it the first chipset with a dedicated neural processing unit (NPU). Then, Apple unveiled the A11 Bionic chip, which powers the iPhone 8, 8 Plus and X. The A11 Bionic features a neural engine that the company says is "purpose-built for machine learning," among other things.

What are they actually good for? In the recent months we've heard a lot about specialized silicon being used for machine learning in mobile devices. Apple's new iPhones have their "neural engine"; Huawei's Mate 10 comes with a "neural processing unit"; and companies that manufacture and design chips (like Qualcomm and ARM) are gearing up to supply AI-optimized hardware to the rest of the industry. What's not clear, is how much all this benefits the consumer. When you're buying your phone, should an "AI chip" be on your wish list?

In the fourth issue of this monthly digest series you can find out how Qualcomm is bringing deep learning and AI to smart devices, why Daimler sent self-driving trucks all across Europe, how to imitate Rembrandt's best work with the help of deep learning, and much more. From the Smithsonian comes news--and a must-see fascinating video--about a painting created using data from more than 168,000 fragments of Rembrandt's work, trained to paint in Rembrandt's signature style. Over the course of 18 months, a group of engineers, Rembrandt experts and data scientists analyzed 346 of Rembrandt's works, then trained a deep learning engine to "paint" in the master's signature style. In order to stay true to Rembrandt's art, the team decided to flex the engine's muscles on a portrait. They analyzed the demographics of the people Rembrandt painted over his lifetime and determined that it should paint a Caucasian male between 30 and 40 years of age, complete with black clothes, a white collar and hat, and facial hair.