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.
• Cost sharing between vendors/SPs and customers can strengthen relationships in a difficult time.
• Calm and deliberate planning by vendors/SPs and customers is key to minimizing impacts to business.
The new tariffs on imported goods in China and the U.S. will have a significant impact on pending and future deals, both for service providers, vendors, and customers. The technology industry has a complex and deeply international supply chain, with U.S. and Chinese companies both utilizing components and intellectual property. Component price increases will lead to sharp increases in product costs. These increases will slow or stall deals as customers may wait and see if the issues can be resolved in a short time frame. Continue reading “Geopolitical Issues Roil IT Sector”→
• Many organizations need help navigating ethical issues related to artificial intelligence (AI), such as privacy laws, unintentional bias, and lack of model transparency, but don’t know where to begin.
• Enterprises can benefit from working with a partner that helps them consider the ethical implications of their AI deployments, but they should keep in mind that issues aren’t static and can evolve over time.
Organizations are eager to enjoy the benefits that AI can bring to them – whether enhanced productivity, or new revenue-generating or enhanced customer experience opportunities. But many are unclear about how to navigate the murky waters of AI and ethics. Changing regulations and privacy laws, concerns over unintentional bias in training data, lack of transparency in AI models, and the dearth of experience with new use cases are difficult challenges to address. Enterprises want to ensure that their adoption of the technology doesn’t cross ethical boundaries, but often don’t know where to begin. Thankfully, the topic is being increasingly addressed by IT services providers. Many organizations, from IBM to Capgemini to Atos are touting that they help their customers implement AI while also considering the ethical implications of their deployment. Continue reading “AI and Ethics: The Waters are Murky, but Help is Available”→
• In order to do AI, IoT, and other big data-dependent projects right, companies are beyond the confines of traditional relational databases.
• Two recent, related partnerships between highly specialized “graph” database developers, Neo4J and TigerGraph, and public cloud platform providers, Amazon and Google, underscores the importance surfacing insights that would otherwise remain hidden within traditional database architectures.
Organizations anxious to put AI to work as a means of driving innovation must first invest in big data. AI algorithms and predictive models are nothing without a constant influx of high quality data. The trouble is that not all data is created equal, at least in terms of its ability to match the demands of a given initiative, be that AI, IoT, mobility, or edge computing.
Such specific demands in turn drive the adoption of highly specialized data architecture, extending down to the database itself. There are traditional relational databases as well as those specializing in key-values, document storage, in-memory processing, time-series evaluation, transaction ledgers, and graph analysis. Each in turn solves very specific problems – e.g., self-driving cars won’t work without an underlying database capable of performing time-series analysis. Continue reading “Graph DB Makers Neo4J and TigerGraph Explore Bring Your Own Database Cloud Options”→
Surprise! BlackBerry is no longer a languishing phone company struggling to remain relevant opposite powerhouse consumer hardware manufacturers Apple, Samsung, Google, et al.
Now a pure software developer with positive revenue numbers, the firm is embarking on an AI-powered journey of trusted and safe communications across a small number of very distinct but highly lucrative markets.
I have always had a soft spot for aging technology. I miss my clear case Apple Newton (stolen) and regret that I can no longer fire up my IBM ThinkPad 701 (the one with the butterfly keyboard). In honor of the 30th anniversary of the Nintendo Game Boy this past week, I even pulled out my Game Boy Pocket (also clear case) for a few rounds of Asteroids and Mortal Kombat II. And even though I never owned a BlackBerry phone, I miss the company’s long-standing dedication to the highly effective but now outdated concept of an actual, qwerty keyboard. Continue reading “Meet the New BlackBerry: Unique Potential Wrapped in the Enigma of AI, Smothered with Cybersecurity Sauce”→
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”→