As Principal Analyst for Collaboration and Conferencing at Current Analysis, Brad analyzes the rapidly expanding use of collaboration software and services as a means of improving business agility, fostering employee optimization and driving business opportunities.
• Late last week AT&T and Samsung together cut the ribbon on a co-developed 5G Innovation Zone that had nothing at all to do with consumer 5G future opportunities.
• Rather, the new facility, housed within Samsung Austin Semiconductor’s Austin Texas fabrication plant, showcased several ways high speed cellular can both modernize and optimize manufacturing processes.
If you travel a few miles northeast of Austin, Texas, you’ll find among the gentle rolling hills an undistinguished 300-acre facility dedicated to the fabrication of semiconductors (aka computer chips) for networking, high performance computing, IoT, and of course mobile devices. And if you look carefully within the foyer of this 20+ year old foundry, you’ll find a somewhat unassuming highly rectangular room peppered with Ikea-styled demonstration tables and plain black monitors that when considered together scream out in all caps: “5G IS VERY REAL, RIGHT NOW!”
• There has been a significant rush among technology providers to make artificial intelligence (AI) a self-service endeavor, to make it available to the broadest possible swath of business users.
• But in so doing, companies are creating unanticipated legal exposure for AI practitioners unprepared to protect AI from human bias.
Salesforce.com has added a new AI learning module to its Trailhead developer education platform with an interesting twist. Rather than teach developers how to build AI outcomes most efficiently, the company’s newest educational module asks that practitioners slow down and focus on creating ethically informed AI solutions.
The new Trailhead educational module entitled, “Responsible Creation of Artificial Intelligence,” calls attention to an often overlooked threat from AI, namely unwitting human biases and intentional human prejudices.
Within these new training materials, Salesforce.com calls on Salesforce.com Einstein developers to adopt its own set of core values of “trust, customer success, innovation, and equality.” The company goes so far as to suggest that developers who fail to adhere to these standards in creating AI algorithms may find themselves in breach of its acceptable use policy.
Why is Salesforce.com referencing an acceptable use policy in conjunction with the ethical use of AI? Surely companies not engaged in outright nefarious endeavors would steer clear of anything overtly illegal in building AI outcomes. Certainly legislative controls such as GDPR and the California Consumer Privacy Act (CCPA) are very clear about what constitutes an unlawful use of consumer data. Companies need only adhere to such policies to avoid potential litigation or censure, right?
• 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.
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”→
• If blockchain is such a good idea, why hasn’t the enterprise IT industry already put it to work at scale?
• As it turns out, the reasons behind blockchain’s slow rate of adoption are as complex and multifaceted as blockchain itself.
Blockchain has been in the news recently with a number of notable players announcing the creation of advantageous partnerships, industry-savvy consortiums, and operationalized use cases. In just the last two days, systems integration powerhouse Accenture announced an innovative mechanism to connect disparate blockchain platforms, while IBM together with Telefónica announced a joint effort to use blockchain to streamline international call routing operations. Both investment and positive speculation remain at an all-time high for blockchain. Continue reading “Is the Idea of Blockchain Too Beautiful to Succeed?”→
• 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.