Daily Tech Digest - March 20, 2021

‘Black Mirror’ or better? The role of AI in the future of learning and development

An AI-assisted learning-development tool can search a variety of sources internally or externally to find content that is relevant to a particular learning or performance outcome. Digital marketers and online publishers have been using AI to generate content for simple stories for years now. Odds are you have read an online article or blog post created by a bot and didn’t even realize it. In the learning space, there are tools such as Emplay and IBM’s Watson that can support this. For example, let’s say a designer wants to create a quick microlearning on how a vacuum pump works. The designer could engage an AI bot to crawl internal or external networks for potential resources — including videos and images. The AI agent then analyzes them, aligning pieces to specific learning outcomes, prioritizing resources for relevance and tagging them by modality. Ultimately, this would free up the designer to focus more on learner-centric design and delivery. ... As you can see, there are many potential benefits to the adoption of AI in the learning space. However, before we invest in AI, it is important to first explore the risks and practical issues of adopting AI across the enterprise.

Facebook is making a bracelet that lets you control computers with your brain

The wristband, which looks like a clunky iPod on a strap, uses sensors to detect movements you intend to make. It uses electromyography (EMG) to interpret electrical activity from motor nerves as they send information from the brain to the hand. The company says the device, as yet unnamed, would let you navigate augmented-reality menus by just thinking about moving your finger to scroll. A quick refresher on augmented reality: It overlays information on your view of the real world, whether it’s data, maps, or other images. The most successful experiment in augmented reality was Pokémon Go, which took the world by storm in 2016 as players crisscrossed neighborhoods in search of elusive Pokémon characters. That initial promise has faded over the intervening years, however, as companies have struggled to translate the technology into something appealing, light, and usable. Google Glass and Snap Spectacles bombed, for example: people simply did not want to use them. Facebook thinks its wristband is more user friendly. Too soon to tell. The product is still in research and development at the company’s internal Facebook Reality Labs, and I didn’t get to have a go.

Uncertainty And Innovation At Speed

As uncertainty continues to rise and the unexpected becomes more common, organizations may not always have the luxury to conduct extensive analysis before acting. Indeed, high uncertainty and rapid change tend to reduce the relevance of the data that companies may have traditionally used for planning. They may need to place bets on multiple possible futures. Above all, they will need capacity for rapid innovation—every day, not just in a crisis. Companies executed the rapid innovations described above by repurposing existing knowledge, resources and technology. A recent article suggests that organizations in all industries may be able to use repurposing to achieve ultrafast innovation to develop new solutions to our current and future challenges. Some innovation thinkers take inspiration from venture capital. Venture capital firms tend to tie funding to the achievement of milestones that reduce investment risk, such as proving technical feasibility or product-market fit. This approach instills a sense of urgency in startup companies: Their very survival may depend on achieving a funding milestone. A crisis such as the COVID-19 pandemic can produce a sense of urgency in even large organizations. But banking on effective innovation in response to a crisis is not a robust strategy.

What is data governance? A best practices framework for managing data assets

When establishing a strategy, each of the above facets of data collection, management, archiving, and use should be considered. The Business Application Research Center (BARC) warns it is not a “big bang initiative.” As a highly complex, ongoing program, data governance runs the risk of participants losing trust and interest over time. To counter that, BARC recommends starting with a manageable or application-specific prototype project and then expanding across the company based on lessons learned. ... Most companies already have some form of governance for individual applications, business units, or functions, even if the processes and responsibilities are informal. As a practice, it is about establishing systematic, formal control over these processes and responsibilities. Doing so can help companies remain responsive, especially as they grow to a size in which it is no longer efficient for individuals to perform cross-functional tasks. ... Governance programs span the enterprise, generally starting with a steering committee comprising senior management, often C-level individuals or vice presidents accountable for lines of business. 

How Google's balloons surprised their creator

In the AI community, there's one example of AI creativity that seems to get cited more than any other. The moment that really got people excited about what AI can do, says Mark Riedl at the Georgia Institute of Technology, is when DeepMind showed how a machine learning system had mastered the ancient game Go – and then beat one of the world's best human players at it. "It ended up demonstrating that there were new strategies or tactics for countering a player that no one had really ever used before – or at least a lot of people did not know about," explains Riedl. And yet even this, an innocent game of Go, provokes different feelings among people. On the one hand, DeepMind has proudly described the ways in which its system, AlphaGo, was able to "innovate" and reveal new approaches to a game that humans have been playing for millennia. On the other hand, some questioned whether such an inventive AI could one day pose a serious risk to humans. "It's farcical to think that we will be able to predict or manage the worst-case behaviour of AIs when we can't actually imagine their probable behaviour," wrote Jonathan Tapson at Western Sydney University after AlphaGo's historic victory.

AI Can Help Companies Tap New Sources of Data for Analytics

Just as Google applications can tell you, on the basis of your home address, calendar entries, and map information, that it’s time to leave for the airport if you want to catch your flight, companies can increasingly take advantage of contextual information in their enterprise systems. Automation in analytics — often called “smart data discovery” or “augmented analytics” — is reducing the reliance on human expertise and judgment by automatically pointing out relationships and patterns in data. In some cases the systems even recommend what the user should do to address the situation identified in the automated analysis. Together these capabilities can transform how we analyze and consume data. Historically, data and analytics have been separate resources that needed to be combined to achieve value. If you wanted to analyze financial or HR or supply chain data, for example, you had to find the data — in a data warehouse, mart, or lake — and point your analytics tool to it. This required extensive knowledge of what data was appropriate for your analysis and where it could be found, and many analysts lacked knowledge of the broader context. However, analytics and even AI applications can increasingly provide context. 

5 Reasons to Make Machine Learning Work for Your Business

The original promise of machine learning was efficiency. Even as its uses have expanded beyond mere automation, this remains a core function and one of the most commercially viable use cases. Using machine learning to automate routine tasks, save time and manage resources more effectively has a very attractive paid of side effects for enterprises that do it effectively: reducing expenses and boosting net income. The list of tasks that machine learning can automate is long. As with data processing, how you use machine learning for process automation will depend on which functions exert the greatest drag on your time and resources. ... Machine learning has also proven its worth in detecting trends in large data sets. These trends are often too subtle for humans to tease out, or perhaps the data sets are simply too large for “dumb” programs to process effectively. Whatever the reason for machine learning’s success in this space, the potential benefits are clear as day. For example, many small and midsize enterprises use machine learning technology to predict and reduce customer churn, looking for signs that customers are considering competitors and trigger retention processes with higher probabilities of success.

Accelerating data and analytics transformations in the public sector

Too often, the lure of exciting new technologies influences use-case selection—an approach that risks putting scarce resources against low-priority problems or projects losing momentum and funding when the initial buzz wears off, the people who backed the choice move on, and newer technologies emerge. Organizations can find themselves in a hype cycle, always chasing something new but never achieving impact. To avoid this trap, use cases should be anchored to the organization’s (now clear) strategic aspiration, prioritized, then sequenced in a road map that allows for deployment while building capabilities. There are four steps to this approach. First, identify the relevant activities and processes for delivering the organization’s mission—be that testing, contracting, and vendor management for procurement, or submission management, data analysis, and facilities inspection for a regulator—then identify the relevant data domains that support them.... Use cases should be framed as questions to be addressed, not tools to be built. Hence, a government agency aspiring to improve the uptime of a key piece of machinery by 20 percent while reducing costs by 5 percent might first ask, “How can we mitigate the risk of parts failure?” and not set out to build an AI model for predictive maintenance.

Quantum computing breaking into real-world biz, but not yet into cryptography

A Deloitte Consulting report echoed Baratz's views, stating that quantum computers would not be breaking cryptography or run at computational speeds sufficient to do so anytime soon. However, it said quantum systems could pose a real threat in the long term and it was critical that preparations were carried out now to plan for such a future. On its impact on Bitcoin and blockchain, for instance, the consulting firm estimated that 25% of Bitcoins in circulation were vulnerable to a quantum attack, pointing in particular to the cryptocurrency that currently were stored in P2PK (Pay to Public Key) and reused P2PKH (Pay to Public Key Hash) addresses. These potentially were at risk of attacks as their public keys could be directly obtained from the address or were made public when the Bitcoins were used. Deloitte suggested a way to plug such gaps was post-quantum cryptography, though, these algorithms could pose other challenges to the usability of blockchains. Adding that this new form of cryptography currently was assessed by experts, it said: "We anticipate that future research into post-quantum cryptography will eventually bring the necessary change to build robust and future-proof blockchain applications."

What Is Open RAN (Radio Access Network)?

Open radio access network (RAN) is a term for industry-wide standards for RAN interfaces that support interoperation between vendors’ equipment. The main goal for using open RAN is to have an interoperability standard for RAN elements such as non-proprietary white box hardware and software from different vendors. Network operators that opt for RAN elements with standard interfaces can avoid being stuck with one vendor’s proprietary hardware and software. Open RAN is not inherently open source. The Open RAN standards instead aim to undo the siloed nature of the RAN market, where a handful of RAN vendors only offer equipment and software that is totally proprietary. The open RAN standards being developed use virtual RAN (vRAN) principles and technologies because vRAN brings features such as network malleability, improved security, and reduced capex and opex costs. ... An open RAN ecosystem gives network operators more choice in RAN elements. With a multi-vendor catalog of technologies, network operators have the flexibility to tailor the functionality of their RANs to the operators’ needs. Total vendor lock-in is no longer an issue when organizations are able to go outside of one RAN vendor’s equipment and software stack.

Quote for the day:

"Challenges in life always seek leaders and leaders seek challenges." -- Wayde Goodall

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