The rapid rise of machine learning and artificial intelligence has resulted in a mass of complex computational and operational challenges that some engineers are trying to tackle with evolutionary algorithms, which work towards multiple optimization objectives concurrently. Industrial Al company NNAISENSE has developed an open-source platform which leverages evolutionary algorithms as the building blocks for cascading machine learning challenges, helping spur industry growth. The platform, called EvoTorch, provides a software tool set that enables developers to experiment with evolutionary algorithms at any scale, without worrying about underlying details. The platform, built on the popular PyTorch and Ray packages, can create evolutionary algorithms that can be parallelized across CPUs or GPUs with little additional effort. "EvoTorch was conceived about five years ago, when the idea came to us to apply evolutionary algorithms to industrial projects and address the intricate challenges associated with scaling." said Dr. Timothy Atkinson, Research Scientist at NNAISENSE, in an interview with Design News.

Today's world is full of opportunities for software engineers and programmers. But the skills needed to be successful today are different from decades ago. Python seems to be the "hot" language, but why? Coders are expected to write code that is freer from bugs than in the past. To learn more about the challenges facing modern software practitioners, Design News reached out to two veterans in the space: Larry Smithmier, Practice Lead, Consultant, at Cognizant Softvision, and Anders Holmberg, Chief Technology Officer at IAR Systems. Here is a portion of that discussion. Design News: What skills do software engineers and programmers need to succeed now and in the near future?

Technology vendors expect that 2020 will be a big year for manufacturing plants to onboard digital systems. While digital systems – IoT, machine learning, 5G, cloud-based systems – have proven themselves as worthwhile investments, they may not get deployed widely. For insight on what to expect in 2020, we turned to Rajeev Gollarahalli, chief business officer at 42Q, a cloud-based MES software division of Sanmina. Gollarahalli sees a manufacturing world that will take solid steps toward digitalization in 2020, but those steps are likely to be incremental rather than revolutionary. Design News: Will 5G increase the pace of digital factory transformation, and where it will have the most impact?

Is emotional AI ready to be a key component of our cars and other devices? Analysts are predicting huge growth for emotional AI in the coming years, albeit with widely differing estimates. A 2018 study by Market Research Future (MRFR) predicted that the "emotional analytics" market, which includes video, speech, and facial analytics technologies among others, will be worth a whopping $25 billion globally by 2025. Tractica has made a more conservative estimate in its own analysis, but still predicted the "emotion recognition and sentiment analysis" market to reach $3.8 billion by 2025. Researchers at Gartner have predicted that by 2022 10 percent of all personal electronic devices will have emotion AI capabilities, either on the device itself or via cloud-based services.

Predictive maintenance based on machine learning has reached the point where it can benefit virtually every manufacturer, big or small, an expert will tell engineers at the upcoming Pacific Design & Manufacturing Show. Kayed Almasarweh, IBM's Watson and cognitive solutions lead, contends that machine learning and artificial intelligence can minimize unplanned downtime, eliminate maintenance guesswork, optimize supply chain management, and reduce warranty costs in products, if used correctly. "This is not only for big manufacturing operations; it's for everybody," Almasarweh told Design News. "Once you get it implemented with the right data, you can get a return on investment almost immediately." Almasarweh will provide a high-level view of predictive maintenance based on machine learning in a session titled, "Applying IoT and Machine Learning for Predictive Maintenance," at the Anaheim Convention Center on February 6th.

Next year, your Thanksgiving grocery shopping may be fulfilled by robots--locally. Takeoff Technologies is piloting a technology that sends robots into a warehouse to fill grocery orders of up to 60 items in five minutes. This operation may finally make the eGrocery concept feasible. That concept goes back to Webvan, an online grocery fulfillment concept that was born--and soon died--during the dot-com boom and bust. Takeoff is partnering with a number of supermarkets to launch the robotic picking of groceries.

Byron Reese believes technology has only truly reshaped humanity three times in history. The first came with the harnessing of fire. And the "third age" came with the invention of the wheel and writing. Reese, CEO and publisher of the technology research company, Gigaom, and host of the Voices in AI podcast, has spent the majority of his career exploring how technology and humanity intersect. He believes the emergence of artificial intelligence is pushing us into a "fourth age" in which AI and robotics will forever transform not only how we work and play, but also how we think about deeper philosophical topics, such as the nature of consciousness. Byron Reese spoke* with Design News about the implications of artificial general intelligence (AGI), the possibility of creating machines that truly think, automation's impact on jobs, and the ways society might be forever transformed by AI.

Intelligent process automation primarily focuses on automating and optimizing business processes that involve people and documents--not processes related to manufacturing automation. The technology is creating essential capabilities that improve business process automation by addressing workflow, forms, and mobile apps. It also is expanding into new and emerging areas, such as robotic process automation, process and machine intelligence, and AI and machine learning. As the technology continues to develop, it is also on a path to provide the missing link in the IoT value chain. The potential is that a virtuous circle of IoT data, insight, decisions, and actions can be leveraged using a holistic set of tools.

Whether it's reducing labor costs, shortening downtime, or increasing all-around productivity, a lot of discussion is happening around the potential of artificial intelligence (AI) to transform manufacturing. But the rise of AI has also come with its own concerns--many around the future of jobs. On one extreme, AI is merely going to transform the way we work and usher in the next industrial revolution. On the opposite, we're all going to be panhandling once robots and software take over our jobs. The reality is somewhere in between.

Imagine if your car could pull itself over when you're drowsy or nauseous, or adjust the temperature and music when gridlock is stressing you out. Maybe it could even refuse to start if it knows you're intoxicated. With advanced ADAS systems already in place and the days of autonomous vehicles on the horizon, a lot of work is being done around sensing and machine learning to help vehicles better understand the roads and the world around them. But Boston-based startup Affectiva thinks more needs to be done around the internal world of the car--specifically the emotional state of the driver. Affectiva has built its business model around creating "emotional AI," algorithms capable of recognizing human emotional states.