Through analytics, companies can reduce overload, attrition, and other costs of collaboration -- and increase its rewards. No question, in a competitive global landscape, collaboration allows companies to serve exacting clients more seamlessly, respond more quickly to changing environments, and innovate more rapidly. But when an organization tries to boost collaboration by adopting a new formal structure, technology, or way of working, it often adds a steady stream of time- and energy-consuming interactions to an already relentless workload, diminishing instead of improving performance. Think about the consequences at an individual level: It's not unusual to feel as if we are just starting our work at 5 p.m., after the daily battery of demands has finally quieted down. Thanks to the plethora of technologies that keep us connected, increasingly integrated global operations, and the need for a multidisciplinary approach to deploying complex products and services, the problem has snowballed over the past decade, with collaborative time demands rising more than 50%. Most knowledge workers and leaders spend 85% or more of their time on email, in meetings, and on the phone.1 Employees struggle with increases in email volume, the proliferation of new collaborative tools, and expectations of fast replies to messages -- with deleterious effects on their quality of work and efficiency. Research tells us that simple distractions like checking a text message fragments our attention more than we realize, and more consuming distractions -- such as answering an email -- can cost us more than 20 minutes to fully regain our focus.2 Even though employees are acutely aware that they're suffering, most organizations don't recognize what's happening in the aggregate.

If you are a fan of Little Britain you are probably familiar with the title of this post, thanks to the different sketches in which a human introduces some data in a desktop PC until, well, the "computer says no". I am currently using some of this short videos as a funny way to introduce one of the main challenges we are facing today when working with Artificial Intelligence systems: algorithm transparency. Explainable AI, or XAI, is an essential requirement of Machine Learning models to understand, trust and manage automated decision systems. Accordind to DARPA, through XAI " New machine-learning systems will have the ability to explain their rationale, characterize their strengths and weaknesses, and convey an understanding of how they will behave in the future. The strategy for achieving that goal is to develop new or modified machine-learning techniques that will produce more explainable models. These models will be combined with state-of-the-art human-computer interface techniques capable of translating models into understandable and useful explanation dialogues for the end user".

It's no secret that big tech companies like Amazon (AMZN), Microsoft (MSFT), and Alphabet (GOOG), the parent company of Google, are investing in digital healthcare. The market opportunity is pretty enticing when you consider that the U.S. alone spent $3.65 trillion on healthcare just last year. Google made the latest headline-grabbing move when it announced that it would buy wearables-maker Fitbit (FIT) in a deal valued at $2.1 billion. Analysts have noted that the acquisition is part of the company's overall strategy to build an ambient intelligent system where Google is omnipresent. Another motive behind the purchase – pending regulatory approvals – is that Fitbit gives Google access to a treasure trove of healthcare data that it can feed to its London-based AI lab DeepMind or its life sciences subsidiary Verily, which is already collaborating on at least one AI healthcare device for remote patient monitoring.

As police embrace new facial recognition technology, many fear false matches could lead to wrongful arrests. The fight over the use of our faces is far from done. A raging battle over controversial facial recognition software used by law enforcement and the civil rights of Americans might be heading to a courtroom. The latest salvo includes the American Civil Liberties Union suing the FBI, the Department of Justice and the Drug Enforcement Agency for those federal agencies' records to see if there is any secret surveillance in use nationwide. The lawsuit, filed Oct. 31, comes as organizations and law enforcement are going toe-to-toe over what is private and what isn't.

In this story, we will visualise the word embedding vectors to understand the relations between words described by the embeddings. This story focuses on word2vec [1] and BERT [2]. To understand the embeddings, I suggest reading a different introduction (like this) as this story does not aim to describe them. This story is part of my journey to develop Neural Machine Translation (NMT) using BERT contextualised embedding vectors. Word embeddings are models to generate computer-friendly numeric vector representations for words.

Github has become the goto source for all things open-source and contains tons of resource for Machine Learning practitioners. We bring to you a list of 10 Github repositories with most stars. TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code.

For the second year in a row, Canada has refused visas to dozens of researchers - most of them from Africa - who were hoping to attend an artificial intelligence (AI) conference in Vancouver. The hassles have caused at least one other AI conference to choose a different country for their next event. The Neural Information Processing Systems conference (NeurIPS), which brings together thousands of experts and researchers from all over the world, will be held in Vancouver next month. Last week, NeurIPS began hearing that several attendees had had their visas denied. It was the second year in a row the conference has had visa troubles.

This August, the Department of Housing and Urban Development put forth a proposed ruling that could potentially turn back the clock on the Fair Housing Act (FHA). This ruling states that landlords, lenders, and property sellers who use third-party machine learning algorithms to decide who gets approved for a loan or who can purchase or rent a property would not be held responsible for any discrimination resulting from these algorithms. The Fair Housing Act (FHA) is a part of the Civil Rights Act of 1968. This stated that people should not be discriminated against for the purchase of a home, rental of a property or qualification of a lease based on race, national origin or religion. In 1974, this was expanded to include gender, and in 1988, disability.

Robotics, increasingly employed across all industrial sectors, enables small and medium-sized enterprises and industries to improve their performance via a flexible, connected production system. In April 2019, a report on French robotics by French MP Bruno Bonnell and robotics expert Catherine Simon drew an encouraging picture of the change taking place in industry. Without sweeping France's shortcomings under the rug, the two authors focused on the country's many assets in the drive to, as the report's title puts it, "make France into an international champion in robotics and intelligent systems". "Over 50% of routine business tasks will be performed by machines by 2025" "Although industrial robots are being introduced more slowly in France than in other countries for the time being, the move is growing at a rate ranging between 6 and 13% per year through 2021," says Thomas Hoffmann, Business Development Director for the Actemium brand, the VINCI Energies network of automated industrial solutions integrators. As part of the move to digitalise businesses, robotics is a promising market, and an increasing number of small and medium-sized businesses and microenterprises will be seeking to automate their processes.

As part of what is likely to be a record-setting defense budget, the Defense Ministry will begin upgrading the Maritime Self-Defense Force's Izumo helicopter carrier next year to enable it to carry fighter jets. The upgrade is in line with the National Defense Guidelines and the Medium-Term Defense Program for fiscal 2019 to 2023, which was adopted by the government in late 2018 and includes the plans to remodel the Izumo so that it can carry U.S.-made, state-of-the-art F-35B stealth fighters, becoming a de facto aircraft carrier. The work is primarily aimed at reinforcing the heat resistance of the Izumo's deck for landings and takeoffs by F-35B jets and is due to start in late fiscal 2019, which ends in March 2020, for completion during fiscal 2021. For fiscal 2020, the ministry has requested a record budget of ¥5.32 trillion, marking the seventh consecutive year the budget request has increased. The sum includes ¥84.6 billion for purchasing six F-35Bs. The MSDF will initially use F-35Bs from the U.S. Marine Corps to train Izumo crew members, as the delivery of the six fighters is not expected to start before fiscal 2024.