Enterprise big data and analytics cuts through the hype to make sense of data collection, storage, management, dissemination and discovery technologies, all employed collectively as a means of realizing corporate efficiencies and uncovering business opportunities.
What do Facebook’s ‘10-Year Challenge,’ Domino’s ‘Points for Pies’ app, and the early detection of diabetic retinopathy all have in common? They prove the difficulty in separating the peril from the promise of AI.
More importantly, however, they illuminate the need for an enforceable code of ethics that includes all ecosystem participants.
• Concerns over the accuracy of facial analytics have prompted IBM to release a dataset of over one million facial images, including facial coding, that can be used to train facial analytics software.
• Improving the results of facial analytics will bolster public confidence in the technology, promoting adoption by enterprises.
IBM has released a dataset of over one million facial images to the global research community to combat bias in facial recognition software. The announcement comes after researchers from MIT and the University of Toronto made claims that a well-known competitor’s product misclassified women at a higher rate than men, with error rates for darker-skinned women far surpassing error rates for lighter-skinned women. With women accounting for roughly half of the world’s population, inaccuracies in their classification present a serious threat to facial recognition adoption. Continue reading “IBM Releases Images to Improve Facial Analytics Accuracy”→
Ford announced plans for self-driving taxis and delivery services; it expects to launch its fleet in Washington, D.C. in 2021.
Ford is one of many companies around the globe that is developing commercial autonomous vehicles.
Soon, if you need a ride to the airport, to the pub, or just around town to run errands, you’ll have another decision to make. Do you hop in a cab? Request an Uber? Or, perhaps… you take a self-driving taxi. What just a few years ago seemed like futuristic technology right out of a sci-fi movie will be here before you know it. Continue reading “Need a Ride? Call a Self-Driving Taxi!”→
• Companies specializing in facial recognition raised sizable amounts of capital from investors in 2018.
• In the coming year, facial recognition will yield new use cases, but will also bring new ethical concerns to the forefront.
Facial recognition is a hot topic. During 2018, several companies active in image recognition, and specifically facial recognition, raised sizable amounts of capital. China-based Sensetime raised an additional $1 billion in September of 2018, bringing the company’s total funding to $2.6 billion. After its Series D funding in July 2018, Megvii’s Face++ had raised a total of $607 million.
During 2019, investment in companies pursing visual recognition and developing new applications for the technology will likely accelerate. Given recent trends, there is a strong possibility that much of this new funding will be flowing into China, which has been very public about its aspirations to lead the global AI arena. Continue reading “Facial Recognition to Spark Lively Debate in 2019”→
At its annual re:Invent 2018 conference, Amazon Web Services rolled out a blinding number of micro-specialized solutions that emphasize ‘best of need’ over ‘best of breed.’
But, there’s a danger lurking in this seeming freedom to adopt and combine capabilities at will, namely a new form of vendor lock-in.
At Amazon Web Services re:Invent 2018 this past week, attendees were treated to an avalanche of product launches and pre-release announcements. On display were three new data management services, four new Internet of Things (IoT) capabilities, eight new storage offerings, and thirteen new machine learning (ML) libraries — all designed to encourage developers to build and deploy solutions on the Amazon Web Services (AWS) platform. And that’s just the software dealing with big data and analytics. Continue reading “Amazon re:Invent 2018: Say Goodbye to Best-of-Breed and Hello to Best-of-Need Applications”→
• Data and analytics have historically belonged to the technological elite, those able to understand the subtleties of inner vs. outer database joins or those willing to learn the difference between a scatter plot and histogram.
• That’s about to change, thanks to a groundswell of innovations emerging from analytics vendors which promise to make data analysis as easy as asking Google Assistant for the day’s weather forecast.
Here’s a very simple proposition to consider: What if we don’t really need citizen data scientists or business-savvy data specialists to realize the current dream shared by most in the enterprise data and analytics marketplace — that dream being the true democratization of data and data-driven insights.
What if instead of asking data specialist to put together a static report — an often iterative and time consuming process — anyone in an organization could type out the simple question, “What were this quarter’s sales numbers?” and get back a timely and accurate data visualization of that quarter’s sales numbers. Continue reading “Tableau Conference 2018: Hello, This is Your Data Calling”→
• At its annual Connect conference in Shanghai, Huawei formally rolled out a new AI portfolio aptly marketed as a full-stack, all-scenario proposition that spans the physical and virtual AI solution set, covering everything from chip to solution.
• Somewhat lost in the sheer size of this portfolio, however, is a hidden gem that seeks to solve one of the biggest challenges facing AI practitioners, namely how to manage the lifecycle of AI apps themselves.
When Huawei sets its sights on a new market opportunity, rarely does the vendor tentatively dip its toes into unknown waters. As has become customary for the globally ambitious technology Chinese provider, new challenges are met all at once with an all-encompassing, all-inclusive portfolio of products that emphasizes immediate availability over future roadmap potentiality. And so it was this week when Huawei introduced its hyphen-heavy full-stack, all-scenario AI portfolio, that begins with the company’s new round of AI Ascend chips and ends with pre-integrated industry solutions.
• What can we learn from global server powerhouse Inspur about customizing servers in order to optimize demanding AI workloads?
• Using Inspur’s joint design and manufacturing (JDM) business model as a guide, it seems current server hardware and software architectures are leaning heavily in the direction of extreme customization, scale and agility.
Just as the promise of AI is very real and likely to significantly alter the way all markets do business, so too is the danger that the decisions we make based upon AI may be flawed, filled with unseen bias, or just plain wrong.
Recent, diverging solutions from IBM and Google to the problem of building trust in AI reveal the sheer magnitude of this multifaceted problem and point to a multi-pronged solution that starts on the drawing board and ends in practice.
Without a doubt, artificial intelligence (AI) has already changed the way consumers interact with technology and the way businesses think about big challenges like digital transformation. In fact, GlobalData research shows that approximately 50% of IT buyers have already prioritized the adoption of AI technologies. And that number is expected to jump to more than 67% over the next two years. Continue reading “How to Succeed in AI by Really, Really Trying”→