A preview of just one of the topics to be covered in a live, virtual event on July 18: Deconstructing Talent Best Practices to Find What Really Works. Artificial Intelligence in the workplace is more than a catch phrase in organizations today. With the pace of technology advancements and usage rapidly accelerating, the acceptance and reliance on such tools is also growing. The first thing HR leaders need to understand is what artificial intelligence (AI) is and what it includes. According to Ben Eubanks, an industry expert from Lighthouse Research, "AI is a term that encompasses multiple types of computerized programs. In fact, someone talking about AI can be discussing anything from facial recognition tools powered by neural networks to machine learning that predicts the best word to use in a subject line to improve email open rates."

Protein engineering through machine-learning-guided directed evolution enables the optimization of protein functions. Machine-learning approaches predict how sequence maps to function in a data-driven manner without requiring a detailed model of the underlying physics or biological pathways. Such methods accelerate directed evolution by learning from the properties of characterized variants and using that information to select sequences that are likely to exhibit improved properties. Here we introduce the steps required to build machine-learning sequence–function models and to use those models to guide engineering, making recommendations at each stage. This review covers basic concepts relevant to the use of machine learning for protein engineering, as well as the current literature and applications of this engineering paradigm.

Machine learning is highly pervasive today so much so that we use it a dozen times a day without even realizing. Machine learning involves getting computers to learn, think, and act on their own without human interference. As described by Google, "Machine learning is the future." With an increasing number of humans becoming addicted to their machines, the future of machine learning looks very bright. We are indeed witnesses to a new revolution which is taking over the world owing to its immense potential.

Sure, world is crying out loud that big-data's biggest problem will be resources. Demand has skyrocketed and everyone in the world is going into tailspin in meeting that demands. Companies are going frantic and overspending to hire data scientists to secure themselves from any upcoming shortfall. This is nothing but a sign that world needs our robot algorithm friends to pacify some demand and increase credibility to new paradigms. Who could forget Steve Balmer's famous quote comparing Big Data as a Machine Learning problem.

BlackBerry Cylance is pleased to announce the debut of CylanceGUARD, a comprehensive security solution that delivers continuous threat prevention across the entire enterprise and response automation powered by BlackBerry Cylance's groundbreaking artificial intelligence platform. CylanceGUARD is a 24x7 managed detection and response (MDR) offering that provides actionable intelligence to prevent and respond to threats quickly, minimizing alert fatigue while delivering the context required to streamline investigations led by world-class threat hunting and incident response experts. The solution also provides an advanced orchestration engine with custom filters to reduce false positives and alert fatigue, bolstering an organization's security posture by providing automated remediation rules to reduce the time lost to manual incident response actions. CylanceGUARD provides proactive threat hunting processes beyond simple alert management, freeing up human resources for other security initiatives and instantly maturing a customer's security program. CylanceGUARD increases overall environment visibility and simplifies complex workflows to dramatically reduce dwell time in identifying and remediating attacks and the proliferation of system intrusions.

From automated eye scans to analysing the cries of new-born babies, faster drug development to personalised medicine, artificial intelligence (AI) promises huge advances in the field of healthcare. At the recent AI for Good Summit in Geneva, Switzerland, we were told how AI could speed up the development of new drugs, lead to personalised medicine informed by our genomes, and help diagnose diseases in countries suffering from underdeveloped health services and a chronic shortage of doctors. But there are two main obstacles preventing access to this utopian destination. One is that the AI being applied to the world's health problems isn't quite good enough yet. The other related issue is the lack of good quality digital data - less than 20% of the world's medical data is available in a form that AI machine learning algorithms can ingest and learn from, the WHO estimates.

Artificial intelligence (AI) is impacting every industry, and supply chain management is no different. While companies now have access to nearly unlimited data, I believe just having data is not enough to improve supply chain performance. Instead, we can leverage artificial intelligence to use this data to make the right decision to move forward faster, smarter and cheaper. Without the good data, however, AI may just as well be making the wrong decision. The definition of AI can be difficult to pin down.

It is particularly focused on the use of artificial intelligence (AI) and/or social media, and the ethical implications and considerations of doing so.

Twitter @tarantulae 4. Uncertainty in Deep Learning - Christian S. Perone (2019) Uncertainties Bayesian Inference Deep Learning Variational Inference Ensembles Q&A Section I Uncertainties 5. Uncertainty in Deep Learning - Christian S. Perone (2019) Uncertainties Bayesian Inference Deep Learning Variational Inference Ensembles Q&A Knowing what you don't know It is correct, somebody might say, that (...) Socrates did not know anything; and it was indeed wisdom that they recognized their own lack of knowledge, (...).

In 1238, the medieval Spanish ruler Alfonso X of Castile published a tome called Libro de los Juegos, or The Book of Games. It consisted of 97 parchment pages, many with beautiful color illustrations, and contains the earliest descriptions of games such as chess, dice, and backgammon.