Raspberry Pi is a capable little machine, but if you're interested in developing your own embedded machine-learning applications, training custom models on the platform has historically been tricky due to the Pi's limited processing power. But things have just taken a big step forward. Yesterday, Edge Impulse, the cloud-based development platform for machine learning on edge devices, announced its foray into embedded Linux with full, official support for the Raspberry Pi 4. As a result, users can now upload data and train their own custom machine-learning algorithms in the cloud, and then deploy them back to their Raspberry Pi. SEE: C programming language: How it became the foundation for everything, and what's next (free PDF) (TechRepublic) Four new machine-learning software development kits (SDKs) for Raspberry Pi are available week, including C, Go, Node.js and Python, allowing users to program their own custom applications for inferencing. Support for object detection has also been added, meaning Raspberry Pi owners can use camera data captured on their device to train their own custom object detection algorithms, instead of having to rely on'stock' classification models.

Modern machine learning (ML) has become an important tool in a very short time. We're using ML models across our organisations, either rolling our own in R and Python, using tools like TensorFlow to learn and explore our data, or building on cloud- and container-hosted services like Azure's Cognitive Services. It's a technology that helps predict maintenance schedules, spots fraud and damaged parts, and parses our speech, responding in a flexible way. SEE: Prescriptive analytics: An insider's guide (free PDF) (TechRepublic) The models that drive our ML applications are incredibly complex, training neural networks on large data sets. But there's a big problem: they're hard to explain or understand.

It includes the company's Helium technology, which addresses machine-learning (ML) applications. Arm estimates that by 2022, more than 20% of IoT endpoint devices will have ML support. The new specification also includes new signal-processing debug features as well as reliability, availability, and serviceability (RAS) extensions. The new enhancements can be added to existing Cortex-M4 and Cortex-M7 as well as the new Cortex-M33 and Cortex-M35P. Enhancements can be added individually to new designs, allowing developers to take advantage of features selectively.

Next banks must dive headlong into the data. They already have a deep understanding of market and other data that flow in and out every day, but machine-learning applications are introducing vast quantities of new types of social media and customer-interface data that need to be catalogued and monitored. These new data forms require the same level of governance as trading and other financial data. Individuals or teams must be relentless in screening out anything that could bias a machine-learning application's results.

Jeanne Ross is principal research scientist for MIT's Center for Information Systems Research. Because, as with enterprise systems, AI inserted into businesses drives value by improving processes through automation. An AI application might allow financial analysts to spend less time extracting data on financial performance, but it adds value only if someone spends more time considering the implications of that performance. Jeanne Ross is principal research scientist for MIT's Center for Information Systems Research.

Enterprises today rely on data as the foundation of business success, whether the goal is to better understand customers, build new or better products and services, or manage cost and risk. Data is now the prime raw material for creating value; across all industries, it's the norm to hold vast stores of data. An issue that remains unresolved, however, is how well and how efficiently data can be applied. Firms are still wrestling with the challenge of making big data work for them, in use cases ranging from enterprise analytics, customer 360, and product personalization to revenue assurance and fraud detection. All the data in the world has no value unless it's accessible and actionable.

Jeanne Ross is principal research scientist for MIT's Center for Information Systems Research. There is no question that artificial intelligence (AI) is presenting huge opportunities for companies to automate business processes. However, as you prepare to insert machine learning applications into your business processes, I'd recommend that you not fantasize about how a computer that can win at Go or poker can surely help you win in the marketplace. A better reference point will be your experience implementing your enterprise resource planning (ERP) or another enterprise system. Yes, effective ERP implementations enhanced the competitiveness of many companies, but a greater number of companies found the experience more of a nightmare.

When the Netflix series House of Cards premiered in 2013, it quickly became the most downloaded content in the company's history – a statistic that came as no surprise to Netflix executives. They had previously examined a vast pool of Netflix data on subscribers' viewing habits and determined that the show was likely to become a hit even before they purchased it. The wisdom behind Netflix's sure-fire choice came from machine learning, which, loosely defined, is the ability of computers to learn on their own (without being programmed) by using algorithms that churn through large quantities of data. Machine learning's talents aren't limited to picking the next TV blockbuster, either. Consider some of the more down-to-earth uses that we already take for granted today. Have you noticed how spam e-mails have almost disappeared from your inbox?

When the Netflix series House of Cards premiered in 2013, it quickly became the most downloaded content in the company's history – a statistic that came as no surprise to Netflix executives. They had previously examined a vast pool of Netflix data on subscribers' viewing habits and determined that the show was likely to become a hit even before they purchased it. The wisdom behind Netflix's sure-fire choice came from machine learning, which, loosely defined, is the ability of computers to learn on their own (without being programmed) by using algorithms that churn through large quantities of data. Machine learning's talents aren't limited to picking the next TV blockbuster, either. Consider some of the more down-to-earth uses that we already take for granted today.

The March issue of Health Affairs demonstrates the potential of health care delivery system innovation to improve value for both patients and clinicians. Technology innovations such as machine learning and artificial intelligence systems are promising breakthroughs to improve diagnostic accuracy, tailor treatments, and even eventually replace work performed by clinicians, especially that of radiologists and pathologists. Machine-learning systems infer patterns, relationships, and rules directly from large volumes of data in ways that can far exceed human cognitive capacities. As the computational underpinning of tools such as e-mail spam filters, product and content recommendations, targeted advertisements, and, more recently, autonomous vehicles, machine learning is already ubiquitous in many economic sectors. Yet, machine-learning applications are still used sparingly today in the delivery of care.