Another common eDiscovery pitfall is the use of standard approaches for every case. Rather than dig in and discern data minimization and cost estimates for each case, many practitioners use generic formulas. Dubious tenets like "every stage of large cases goes to law firms" or "law firms always manage review for us" still rule the day. Teams automatically slap project planning formulas like 0 to 6 months for ECA, 6 to 12 months for full-blown eDiscovery and 12 to 24 months to finish eDiscovery, motions and trial preparations onto every eDiscovery project.

Organizations today are going through a variety of digital hardships. They are trying to find ways to derive value from data through which they want to achieve specific business outcomes. But doing so is not a cakewalk, it takes a lot of effort from data scientists' end to mine data to carve analytics application for driving innovative and efficient decision-making. In order to make analytics more effective, businesses are replacing traditional data management with an emerging set of practices. These practices are focused on collaboration and automation and are known as data operations, or DataOps. DataOps is a junction of advanced data governance and analytics delivery practices that incorporates the data life cycle.

Newswire) GBT Technologies Inc. (OTCPINK: GTCH) ("GBT", or the "Company"), a company specializing in the development of Internet of Things (IoT) and Artificial Intelligence (AI) enabled networking and tracking technologies, including its GopherInsight wireless mesh network technology platform for both mobile and fixed solutions, announced that it has entered into a Joint Venture ("JV") Agreement with BitSpeed LLC ("BitSpeed"), a software development company located in the Los Angeles area. BitSpeed is currently partnered with a major Cloud Platform (GCP) and has an agreement to supply its software through GCP's Partner Advantage program, where their customers can buy certified solutions directly from GCP. BitSpeed's Concurrency application competes in the Extreme File Transfer market, which is a subset of the larger Managed File Transfer or MFT market. "Managed file transfer is a type of software that allows the transfer of files inside an organization or between multiple organizations. This method is a fast, secure, reliable, and a transparent way of exchanging files, with additional features such as tracking and monitoring. As a result, one can discover the loss of data from a specific point, and also receive an acknowledgement after successful completion of a file transfer process. With the advent of digitalization, companies are becoming heavily dependent on the successful transmission of digital files bearing critical information. Hence, this transmission should be secure, reliable, and quick to run the business process smoothly in real time. The demand for efficient and effective file transfer has been increasing in the past few years."

The recent AI revolution was based on a few important pillars: abundance of data, lower costs in storing it, faster computing speed and distributed computing. To date, however, there still are some critical steps that are missing to achieve true continuous intelligence. Continuous intelligence refers to a design in which real-time analytics is seamlessly integrated within a business operation to support decision automation. Data processing can therefore be used to respond real-time to events. In order to achieve true continuous intelligence we are still missing a couple of key elements, in particular explainable AI and better graph analytics. Deep Learning uses multi-layers neural nets that have been extremely popular in recent years, in particular for computer vision.

In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. For this to occur, the entire community stands in front of the barrier of explainability, an inherent problem of AI techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is acknowledged as a crucial feature for the practical deployment of AI models. This overview examines the existing literature in the field of XAI, including a prospect toward what is yet to be reached. We summarize previous efforts to define explainability in Machine Learning, establishing a novel definition that covers prior conceptual propositions with a major focus on the audience for which explainability is sought. We then propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at Deep Learning methods for which a second taxonomy is built. This literature analysis serves as the background for a series of challenges faced by XAI, such as the crossroads between data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to XAI with a reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.

On a cold, sunny October day on the outskirts of Copenhagen, Denmark, a group of men dressed in black gathers outside Brondby Stadium to shoot off a couple of rockets, raise their fists and shout about how the home team will soon beat -- and beat up -- the visiting archnemesis, FC Copenhagen. Police are out in force, riot helmets at the ready. Brondby-Copenhagen matches have a history of leading to vandalism, arrests and general mayhem. An attempted photo of the group gets a gloved hand in the face. "You need to stop," says the hand's black-clad owner, before he disappears back into the crowd.

Veena Dubal is an unlikely star in the tech world. A scholar of labor practices regarding the taxi and ride-hailing industries and an Associate Professor at San Francisco's U.C. Hastings College of the Law, her work on the ethics of the gig economy has been covered by the New York Times, NBC News, New York Magazine, and other publications. She's been in public dialogue with Naomi Klein and other famous authors, and penned a prominent op-ed on facial recognition tech in San Francisco -- all while winning awards for her contributions to legal scholarship in her area of specialization, labor and employment law. At the annual symposium of the AI Now Institute, an interdisciplinary research center at New York University, Dubal was a featured speaker. The symposium is the largest annual public gathering of the NYU-affiliated research group that examines AI's social implications.

"Bias in AI" refers to situations where machine learning-based data analytics systems discriminate against particular groups of people. This discrimination usually follows our own societal biases regarding race, gender, biological sex, nationality, or age (more on this later). Just this past week, for example, researchers showed that Google's AI-based hate speech detector is biased against black people. In this article, I'll explain two types of bias in artificial intelligence and machine learning: algorithmic/data bias and societal bias. I'll explain how they occur, highlight some examples of AI bias in the news, and show how you can fight back by becoming more aware.

A growing number of leaders see AI as not just a business opportunity but also as a strategic risk. How do you ensure that your competitors don't figure out how to successfully use it before you do? This year's Artificial Intelligence Global Executive Study and Research Report from MIT SMR and BCG shows early AI winners are focused on organization-wide alignment, investment, and integration. The good news is that there are more women in top-level positions at U.S. businesses than at any other point in history. But a study has found that many women face the biggest obstacle to reaching the top of the corporate ladder early in their careers, with fewer women than men getting the opportunity to take their first step into management.

Facebook just lost a battle in its war to stop a $35 billion class action lawsuit regarding alleged misuse of facial recognition data in Illinois. Today it was denied its request for an en banc hearing before the full slate of ninth circuit judges that could have halted the case. Now the case will go to trial unless the Supreme Court intercedes. The suit alleges that Illinois citizens didn't consent to having their uploaded photos scanned with facial recognition and weren't informed of how long the data would be saved when the mapping started in 2011. Facebook could face $1,000 to $5,000 in penalties per user for 7 million people, which could sum to a maximum of $35 billion.