Artificial intelligence (AI) and machine learning have been the focal point of attention for the last decade. While it's natural for progressive business owners to get distracted by the hype of AI, unfortunately, current AI – machine learning solutions are not well suited for small businesses. But they will be, over time. Let's try to gain a deeper understanding of where AI solutions are presently, and how they will eventually become viable to small businesses. There are three different levels of expertise and investment that determine what kind of businesses can access AI solutions.

In the first part of the Autodidacts path to a MSc level in AI/Machine Learning, using UCL's MSc as a lighthouse to guide us through the rough waters of building a Machine Learning MSc curriculum, we had a look at some of the most established and helpful resources for a beginning ML engineer. Moving on to the second part of our attempt to build a curriculum for the autodidact enthusiast of Machine Learning, we will dive into one of the hot topics during the past decade. This is no other than Deep Learning. Although technically a sub-category of Machine Learning, Deep Learning has evolved into its own paradigm and has earned the title of'one of the pillars of ML' and for good reasons. The past decade has seen a huge number of successful applications and technological advancements that utilise Deep Learning.

Braze delivers customer experiences across email, mobile, SMS, and web. Customers, including Burger King, Delivery Hero, HBO Max, Mercari, and Venmo, use the Braze platform to facilitate real-time experiences between brands and consumers in a more authentic and human way. And we do it at scale – each month, hundreds of billions of messages are sent to a network of over 3 billion active users through Braze. Braze was named a Leader in the Forrester Wave: Cross-Channel Campaign Management (Independent Platforms), Q3 2021, and was named to the Forbes Cloud 100 list for the fourth consecutive year. The company has also been selected as one of Fortune's Best Workplace for Millennials in 2021, and was ranked #20 on Fortune's Best Medium Sized Workplaces in 2021.

IEEE says the most important technologies in 2022 will be AI/machine learning, cloud computing and 5G wireless. That comes in a new report published by the large technical professional organization titled "The Impact of Technology in 2022 and Beyond: an IEEE Global Study," based on an October survey of 350 chief information officers, chief technology officers and technology leaders from the U.S., U.K., China, India and Brazil who were asked about key technology trends, priorities and predictions for 2022 and beyond. "Among total respondents, more than one in five (21 percent) say AI and machine learning, cloud computing (20 percent), and 5G (17 percent) will be the most important technologies next year," IEEE said in a Nov. 18 announcement. "Because of the global pandemic, technology leaders surveyed said in 2021 they accelerated adoption of cloud computing (60 percent), AI and machine learning (51 percent), and 5G (46 percent), among others." The report includes respondent data for 12 questions, starting off with: "Which will be the most important technology in 2022?"

Artificial intelligence (AI) machine learning is rapidly being deployed to help accelerate neuroscience, psychology, and psychiatry research. A new study published in Molecular Psychiatry by researchers affiliated with Yale University shows how AI machine learning can identify patterns of neural connections in the brain associated with aggressive behavior in children. According to the Yale researchers, this study is a first of its kind. "Disruptions in frontoparietal networks supporting emotion regulation have been long implicated in maladaptive childhood aggression," wrote the researchers. "However, the association of connectivity between large-scale functional networks with aggressive behavior has not been tested."

OpenAI has recently published an important work, focused on the alignment problem, the problem of ensuring that general-purpose AI and machine learning systems align with human intentions. The "Paperclip Maximizer" is a famous example of alignment gone wrong. To test scalable alignment methods, OpenAI trained a model to summarize entire books, as described in their blog on KDnuggets: Scaling human oversight of AI systems for difficult tasks – OpenAI approach. OpenAI model works by first summarizing small sections of a book, then summarizing those summaries into a higher-level summary, and so on. The results were pretty amazing, so we have asked OpenAI to summarize two top KDnuggets blogs from last year, and here are the summaries.

I recently had the privilege of participating on a panel with several AI/Machine Learning experts. There were many great questions, but most were related to how to most effectively establish an AI/Machine Learning (AI/ML) in a large organization. This gave me an opportunity to reflect on my own experiences helping large enterprise accelerate their AI/Machine Learning journey, and, more specifically, assess what worked, and perhaps just as importantly, what did not work. I have condensed these into a few simple "lessons learned" that hopefully will be useful to you on your organization's AI/ML journey. In my experience, your models will never be perfect.

AI Machine Learning is being debated due to the "update problem" of adaptiveness. Humans typically learn new things on-the-fly. Let's use jigsaw puzzles to explore the learning process. Imagine that you are asked to solve a jigsaw puzzle and you've not previously had the time nor inclination to solve jigsaw puzzles (yes, there are some people that swear they will never do a jigsaw puzzle, as though it is beneath them or otherwise a useless use of their mind). Upon dumping out onto the table all the pieces from the box, you likely turn all the pieces right side up and do a quick visual scan of the pieces and the picture shown on the box of what you are trying to solve for.

Ground-breaking changes are happening on the edge of computing. We're long past the days when all analytics can be centralized in datacenters or even in the cloud. It's an increasingly decentralized world where analytics has to take place in real time right where individual sensors are, or in the fog when there's a need to collect information from multiple devices for fast insights. We recently sat down to talk with renowned technology consultant Marc Staimer about computing in the edge, the fog, and the core. In part two of our conversation, we're going to take a more in-depth look at matching the analytics requirements to the location of the analysis, especially when those analytics need to take place on the edge or in the fog.

Are you curious about AI/Machine Learning and the future of this tech in start-ups and big business? Come listen to Emma Zohner and her research background in this field as well as her view on the future of AI/machine learning.