For far too long, sales reps and commercial managers in the pharmaceutical industry have had their responsibilities eroded by wave after wave of IT implementations, each one supposedly making life easier - but doing the exact opposite. The time has come for a more intelligent solution. It's time for technology to empower, not overrule. It's time for sales and marketing managers to regain control. Single black-box'next best actions' which dictate and disempower If you want to re-empower your sales and marketing staff with a smarter generation of technology, enter your details.

Disruption to supply chains as the pandemic swept the globe has led many companies to reevaluate how well-equipped they are to handle system-wide volatility across networks. How is anyone to make sense of demand and supply patterns and manage overall health in the midst of this pandemic -- which has introduced a level of uncertainty that current enterprise tools are not designed to process? Working closely with our customers on a daily basis, we are being asked to help make sense of their demand signals across complex networks and hierarchies. We are also helping them predict and respond to impending supply imbalances within their 0-12 week execution windows, a critical source of value leakage and especially pertinent in current times. Faced with this fast-paced, multi-dimensional chess-game, customers need clear planning recommendations that improve fill rates, reduce inventory, minimize write-offs and control logistics spend.

The integrity and trustworthiness of data or any other master entity is enforced via data quality rules. Customers no longer want to rely on hand crafted rules that can number in the thousands, which in turn also need a lot of maintenance. Riding on the machine learning (ML) wave, customers can break free from their rule-based business logic and rely on data driven decisions within product information management systems (PIM). These processes are necessary for decreasing effort and saving time and costs. The IBM InfoSphere Master Data Management (MDM) suite offers these ML capabilities in IBM MDM Product Master to help organize product and service information across the enterprise. As a PIM solution, IBM Product Master (formerly IBM InfoSphere Master Data Management Collaborative Edition) aggregates information from any upstream system, enforces business processes to ensure data accuracy and consistency, and synchronizes trusted information with downstream systems.

Companies across industries are exploring and implementing artificial intelligence (AI) projects, from big data to robotics, to automate business processes, improve customer experience, and innovate product development. According to McKinsey, "embracing AI promises considerable benefits for businesses and economies through its contributions to productivity and growth." But with that promise comes challenges. Computers and machines don't come into this world with inherent knowledge or an understanding of how things work. Like humans, they need to be taught that a red light means stop and green means go.

We were delighted to be joined by Lex Fridman at the San Francisco edition of the Deep Learning Summit, taking part in both a'Deep Dive' session, allowing for a great amount of attendee interaction and collaboration, alongside a fireside chat with OpenAI Co-Founder & Chief Scientist, Ilya Sutskever. The MIT Researcher shared his thoughts on recent developments in AI and its current standing, highlighting its growth in recent years. Lex then referenced, Lee Sedol, the South Korean 9th Dan GO player, whom at this time is the only human to ever beat AI at a video game, which has since become somewhat of an impossible task, describing this feat as a seminal moment and one which changed the course of not only deep learning but also reinforcement learning, increasing the social belief in the subsection of AI. Since then, of course, we have seen video games and tactically based games, including Starcraft become imperative in the development of AI. The comparison of Reinforcement Learning to Human Learning is something which we often come across, referenced by Lex as something which needed addressing, with humans seemingly learning through "very few examples" as opposed to the heavy data sets needed in AI, but why is that?

In the last few months, millions of dollars have been stolen from unemployment systems during this time of immense pressure due to coronavirus-related claims. A skilled ring of international fraudsters has been submitting false unemployment claims for individuals that still have steady work. The attackers use previously acquired Personally Identifiable Information (PII) such as social security numbers, addresses, names, phone numbers, and banking account information to trick public officials into accepting the claims. Payouts to these employed people are then redirected to money laundering accomplices who pass the money around to veil the illicit nature of the cash before depositing it into their own accounts. The acquisition of the PII that enabled these attacks, and the pattern of money laundering that financial institutions failed to detect highlight the importance of renewed security.

In the last few months, millions of dollars have been stolen from unemployment systems during this time of immense pressure due to coronavirus-related claims. A skilled ring of international fraudsters has been submitting false unemployment claims for individuals that still have steady work. The attackers use previously acquired Personally Identifiable Information (PII) such as social security numbers, addresses, names, phone numbers, and banking account information to trick public officials into accepting the claims. Payouts to these employed people are then redirected to money laundering accomplices who pass the money around to veil the illicit nature of the cash before depositing it into their own accounts. The acquisition of the PII that enabled these attacks, and the pattern of money laundering that financial institutions failed to detect highlight the importance of renewed security.

We describe a computer program that implements an algorithm to verify the consistency and completeness of knowledge bases built for the Lockheed expert system (LES) shell. The algorithms described here are not specific to this particular shell and can be applied to many rule-based systems. The computer program, which we call CHECK, combines logical principles as well as specific information about the knowledge representation formalism of LES. CHECK identifies inconsistencies in the knowledge base by looking for redundant rules, conflicting rules, subsumed rules, unnecessary IF conditions, and circular rule chains. Checking for completeness is done by looking for unreferenced attribute values, illegal attribute values, dead-end IF conditions, dead-end goals and unreachable conclusions.

Hardly anyone relying on data can say their data is perfect. There is always that difference between the dataset you have and the dataset you wish you had. This difference is what Data Quality is all about. Data quality problem exists everywhere where data is used: in tech and non-tech businesses, in the public sector, in engineering, in science. Each of these domains has its data specifics and its own set of data quality criteria.