Artificial intelligence is an increasingly prominent part of our lives, in areas you may not even think about. Chances are you've had a travel problem in the last year or two, caused by the many disruptions the COVID pandemic has wrought on the industry. When you messaged your airline's Facebook page, did you encounter a bot? I bet your school-age children ask your smart speaker at home 1,000,000 questions per day, or ask your respective brand's speaker to play 46,789 songs per day. I bet many of you reading this have applied for a job during the pandemic, when the job market has very much favored job seekers.

Interactions provides Intelligent Virtual Assistants that seamlessly assimilate conversational AI and human understanding to enable businesses to engage with their customers in highly productive and satisfying conversations. With flexible products and solutions designed to meet the growing demand for unified, optichannel customer care, Interactions is delivering unprecedented improvements in the customer experience and significant cost savings for some of the largest brands in the world. The company recently launched Trustera, a real-time, audio-sensitive redaction platform. Trustera preemptively identifies and protects sensitive information like credit card numbers and solves the biggest compliance challenge in today's contact-center environment: protecting a customer's Payment Card Information (PCI) anywhere it appears during a call. The platform is designed to make the customer experience more trustworthy, secure and seamless.

Advancement in diffusion models, the latest cutting edge AI trend that generates a multitude of unique high-resolution images, has increased public interest in generative models massively. The Intellectual Property Rights surrounding artificial intelligence is a hot topic. There are also larger legal ramifications of the illegal use of images, movies, videos, etc in the creation of these models. Ongoing advancements to the use cases and data sources will extend deeper and further than just image and image generation, into the intellectual property rights surrounding individual information. Several standards groups have taken note and are working to create a more level playing field for consumers and businesses leveraging this technology.

Here's a new title that is a "must have" for any data scientist who uses the R language. It's a wonderful learning resource for tree-based techniques in statistical learning, one that's become my go-to text when I find the need to do a deep dive into various ML topic areas for my work. The methods discussed represent the cornerstone for using tabular data sets for making predictions using decision trees, ensemble methods like random forest, and of course the industry's darling gradient boosting machines (GBM). Algorithms like XGBoost are king of the hill for solving problems involving tabular data. A number of timely and somewhat high-profile benchmarks show that this class of algorithm beats deep learning algorithms for many problem domains.

Anomaly detection in the context of data science is detecting a data sample that is out of the ordinary and does not fit into the general data pattern (or an outlier). This deviation can result from a rare occurrence or an outlying event. Identifying these samples, called anomaly detection, is an integral part of any monitoring system. Anomaly detection has been traditionally done manually by inspection, which is a tedious process typically done by experts with significant domain knowledge. Anomaly detection is used in a wide variety of applications.

Deci, the deep learning company building the next generation of AI, announced a breakthrough performance on Intel's newly released 4th Gen Intel Xeon Scalable processors, code-named Sapphire Rapids. By optimizing the AI models which run on Intel's new hardware, Deci enables AI developers to achieve GPU-like inference performance on CPUs in production for both Computer Vision and Natural Language Processing (NLP) tasks. Deci utilized its proprietary AutoNAC (Automated Neural Architecture Construction) technology to generate custom hardware-aware model architectures that deliver unparalleled accuracy and inference speed on the Intel Sapphire Rapids CPU. For computer vision, Deci delivered a 3.35x throughput increase, as well as a 1% accuracy boost, when compared to an INT8 version of a ResNet50 running on Intel Sapphire Rapids. For NLP, Deci delivered a 3.5x acceleration compared to the INT8 version of the BERT model on Intel Sapphire Rapids, as well as a 0.1 increase in accuray.

In this contributed article, April Miller, a senior IT and cybersecurity writer for ReHack Magazine, discusses how data analytics relies on vast amounts of information from different sources to be effective. The same often goes for AI. Unified analytics consolidates these disparate data sources and analytics pipelines into a single platform, using AI to automate that process. The article shows how this consolidation and automation can improve your business.

Our friends over at cnvrg.io have released a new eBook, "The Machine Learning Infrastructure Blueprint," answering common questions most machine learning teams are asking about best practices for building a scalable machine learning infrastructure. Machine learning has matured and now data science teams demand more from their machine learning infrastructure. In the past machine learning was mostly for research, today it is driving businesses. While the base of a machine learning platform remains the same (manage, monitor, track experiments and models) to achieve scalability, elasticity and operationalization of machine learning development there are various capabilities that need to be considered before building a modern machine learning infrastructure. Today's machine learning infrastructures must be built for production, with as little technical debt as possible to accelerate machine learning development.

In this contributed article, Ittai Dayan, co-founder and CEO of Rhino Health, believes that while traditional machine learning has huge potential for medical researchers, its major shortcoming is the vast amount of centralized data collection that’s required, and the privacy issues this creates. Federated learning has been suggested as a potential solution to this problem. This is a novel ML technique that is able to access data held across numerous decentralized servers (such as data held by individual hospitals), with the data never leaving these servers and remaining completely anonymous.

In this contributed article, Ankush Gupta and Kavya Shree of FischerJordan, explore the scope, use cases and challenges of AutoML and how data scientists and AutoML can have a future together. The authors discuss the causes driving the use of AutoML, the benefits and challenges associated, and major providers in the space. They conclude by analyzing the parts of the data science and ML process that can/cannot be automated and if AutoML will replace data scientists / both will go hand-in-hand.