The book walks readers through the machine learning process from start to finish, starting with data, feature engineering, model training, and deployment on Google Cloud. It also discusses best practices on when to pick a custom model vs AutoML or pretrained models with Vertex AI platform. All technologies such as Tensorflow, Kubeflow, and Vertex AI are presented by way of real-world scenarios to help you apply the theory to practical examples and show you how IT professionals design, build, and operate secure ML cloud environments.

For a forthcoming book with Nitin Mittal of Deloitte, I've been researching companies that are "All In on AI," as the book will be titled. These are companies who have made substantial and long-term bets on the notion that AI will revolutionize the way they do business. In several of the companies, the CEOs have been heavily engaged in the AI-driven transformation process. One of them is Piyush Gupta, the CEO of Singapore-based DBS Bank, whom I have written about elsewhere as an AI leader. Another is Peter Ma, the founder and chairman of Ping An, which is the largest private-sector firm in China.

For a forthcoming book with Nitin Mittal of Deloitte, I've been researching companies that are "All In on AI," as the book will be titled. These are companies who have made substantial and long-term bets on the notion that AI will revolutionize the way they do business. In several of the companies, the CEOs have been heavily engaged in the AI-driven transformation process. One of them is Piyush Gupta, the CEO of Singapore-based DBS Bank, whom I have written about elsewhere as an AI leader. Another is Peter Ma, the founder and chairman of Ping An, which is the largest private-sector firm in China. Ping An makes extensive use of AI to drive its five ecosystems, and my sources tell me that Ma is heavily involved in the decisions around the technology.

The increasingly rapid movement of technology out of back offices and into the hands of most of the world's population has profound effects. Even though the pandemic is widely credited with accelerating digital uptake, the full progression is really just beginning to shift from linear to exponential. As bank technology consultant Shanker Ramamurthy likes to point out, most of the world's population now walks around with "trillion-dollar" computers in their pockets. Ramamurthy is Global Managing Partner, Banking for IBM's Global Business Services group, the giant tech company's banking and consulting practice. Adding to that, IBM itself has been humbled by the same forces of digital transformation impacting the businesses it serves.

IBM has outlined principles to promote transparency -- and foster public trust -- in the way companies use artificial intelligence. The principles call on banks and other organizations to designate a lead AI official, own up to their use of the technology, explain it and test it for bias. Bankers say they're already on it. IBM unveiled the principles last month at Davos through its new IBM Policy Lab. The goal was to provide guidance for developing intelligent policy that will provide societal protections without stifling innovation.

Imagine a visit to your doctor's office in which your physician asks you how you've been feeling, whether your medication is working or if the shoulder pain from an old fall is still bothering you -- and his or her focus is entirely on you and that conversation. The doctor is looking at you, not at a computer screen. He or she isn't moving a mouse around hunting for an old record or pecking on the keyboard to enter a diagnosis code. This sounds like an ideal scenario, but as most people know from their own visits to the doctor, it's far from the norm today. But experts say that in an exam room of the future enhanced by artificial intelligence, the doctor would be able to call up a lab result or prescribe a new medicine with a simple voice command.

The shift to value-based care often has C-suite executives wondering how best to support the transition, while ensuring a solid return on investment in the long term. And the key to improving care value is to ensure an organization has the right tools to support patients throughout the care cycle. To Sheela Ramamurthy, VirtualHealth chief client officer, organizations need to focus on the most vulnerable care populations to make the biggest impact. And while there's a lot of hype around artificial intelligence and robotics, the tools that run in the background will make the greatest impact on those care populations. "Solutions that bring data together to address whole-person care not only bridge gaps in both care and services, but help patients stay healthy and out of the hospital or emergency room," said Ramamurthy. "Actionable insights provided through care management technology help ensure patients with the greatest needs get timely access to services."

"One of these things is not like the others," the television show Sesame Street taught generations of children. Let's move to the next level: "One or more of these things may or may not be like the others, and those variances may or may not represent systems vulnerabilities, failed patches, configuration errors, compliance nightmares, or imminent hardware crashes." Looking through gigabytes of log files and transactions records to spot patterns or anomalies is hard for humans: it's slow, tedious, error-prone, and doesn't scale. Fortunately, it's easy for artificial intelligence (AI) software, such as the machine learning algorithms built into Oracle Management Cloud. What's more, the machine learning algorithms can be used to direct manual or automated remediation efforts to improve security, compliance, and performance.

"One of these things is not like the others," the television show Sesame Street taught generations of children. Let's move to the next level: "One or more of these things may or may not be like the others, and those variances may or may not represent systems vulnerabilities, failed patches, configuration errors, compliance nightmares, or imminent hardware crashes." Looking through gigabytes of log files and transactions records to spot patterns or anomalies is hard for humans: it's slow, tedious, error-prone, and doesn't scale. Fortunately, it's easy for artificial intelligence (AI) software, such as the machine learning algorithms built into Oracle Management Cloud. What's more, the machine learning algorithms can be used to direct manual or automated remediation efforts to improve security, compliance, and performance.

Sparse distributed memory is an auto-associative memory system that stores high dimensional Boolean vectors. Here we present an extension of the original SDM, the Integer SDM that uses modular arithmetic integer vectors rather than binary vectors. This extension preserves many of the desirable properties of the original SDM: auto-associativity, content addressability, distributed storage, and robustness over noisy inputs. In addition, it improves the representation capabilities of the memory and is more robust over normalization. It can also be extended to support forgetting and reliable sequence storage.