Opinion: Responsible AI starts with higher education
“This new algorithm will need a lot of pictures of people. What if we use a morgue so we don’t have to worry about consent?” Although this is a fictitious example, modern-day tech workers often face similar questions. Why? Because the rise of artificial intelligence based on machine learning has created a new class of sociotechnical challenges. Now is the time for industry and universities to acknowledge these new challenges and step up to meet them. Since the beginning of the technology industry, educational institutions, legislatures, companies, and developers have worked to improve the quality of products and services. The resulting curricula, laws, corporate policies, standards, and development approaches have provided frameworks for engineers and product managers. Emerging technologies require the development of new frameworks. In the early 2000s, industry had to get serious about computer security. Today, we have a new challenge: How do you turn the goal of responsible AI into code?
How to help data scientists adapt to business culture
Businesses themselves don't understand what the data science discipline is, the work backgrounds from where data scientists are coming, and what it's going to take to acculturate these highly trained data engineers to how a business operates and what it needs. Many data scientists have lived their lives in environments funded by university grants that enabled them to pursue highly theoretical projects that are all about the quest for answers but not necessarily about finding definitive solutions for why customers seem to be suddenly favoring another brand, or why your manufactured products are suddenly experiencing more failures. Companies also struggle with integrating data scientists with their existing business and IT workforces. Often, existing business units and IT have little in common with data scientists, and there are no existing workflows that can help them learn how to optimally work together. Another issue is that businesses aren't always sure what (and when) to expect analytics and results out of their big data projects. Successful use cases exist in most industries, but companies still don't have a good feel for knowing when a data science or analytics project is moving forward and when it is stagnating.
20 ways banks can get AI right
Try to create ‘segments of one’ through the collection of the volume and variety of data that can empower you to pursue automated hyper-personalisation. When clients feel that your service is sensitive and responsive to their individual preferences, they will be happy to share more and more information with you. Think about a situation where you offer the client the chance to offer money to a charity through ‘rounding prices up’. For example, the client purchases a coffee for £1.79 and you offer to have the remaining £0.21 put into a charity pot, which, after the client has collected £20, this pot can be given to a charity, which you, as a bank, will match with an equal donation. Let’s say the client is a paediatrician. In this case, the three potential charities the client can choose from should be about health, children, and medical research. Another client is a music teacher, in which case the three choices can be related to classical music, early talent, and education. These elements of hyper-personalisation have to be fully automated and ideally be propelled by some levels of AI.
Understanding the convergence of IoT and data analytics
Simply collecting IoT data is not enough — “Organisations need to turn this data into value in both a batch (using traditional analytics) and real-time context. It is also not desirable, nor possible in some cases, to do all of your processing at the enterprise level (in the cloud or data centre, for example).” As is the nature of IoT devices, decisions will often need to be made in a localised fashion, including on the device itself, and these decisions will be largely driven by models derived from analytical processes and historical data. “The ability to make the edge ‘smarter’, offload compute workloads to the edge for more efficient processing, support localised or independent/disconnected processing, reduce decision latency, and reduce data transfer requirements are all benefits that may be applied to almost any vertical,” continues Petracek. “Analytics, and the operationalisation of analytical models and pipelines, presents a huge opportunity to organisations, especially given the level of real-time information and context that IoT can provide.”
2020 is about digital optimization not digital transformation
Digital optimization isn’t easy as a result of completely different groups are prone to have invested in options that don’t communicate to one another and aren’t straightforward to combine with one another. Further, every group may very well be going digital at a special tempo – these on the front-line coping with clients on daily basis are beneath extra stress than these working the group’s core operations. However, with out digital optimization, organizations will likely be unable to eliminate the silos in its processes even when groups embrace collaboration within the spirit of digital innovation. ... The vital factor to recollect when optimizing digital investments is that the group has one purpose, one mission, and one imaginative and prescient. Hence, the roadmap have to be comprised of straightforward milestones that have an effect, ideally on the enterprise-level. At the tip of the day, organizations should perceive the significance of optimizing their investments in digital, and prioritize it over spends that merely broaden their digital portfolio.
Managing Trade-offs: Prediction, Adaptability and Resilience
One critical new way of working that CEOs must “bottle” is organizational learning through local experimentation and global scaling. Lockdown has not only liberated the CEO, it has also freed local leaders from top-down governance. Often asking for forgiveness, rather than permission, they’ve innovated, disrupted and bullied their way to solutions that surmount obstacles and serve customers. In doing so, local teams have found support from the center. Some global leaders helped scale top solutions across the firm. They reimagined marketing and sales budgets overnight, showing the organization what costs are critical and what are dispensable. They solved huge supply chain issues, teaching the organization how to strengthen its operations. In order to ensure that this burst of experimentation and learning doesn’t become a historical oddity, leading CEOs will systematically protect the fundamental new relationship between global and local. They will set a clear agenda for the core business (or, as we like to call it, “Engine 1”): Continue the same pace of experimentation and learning throughout the long dance.
EY: revolutionising supply chain management with blockchain
While in traditional supply chains production is recorded digitally, when it comes to shipping Brody explains that maintaining information continuity across systems and enterprise boundaries is a challenge, there is “oceans of digital data but only islands of useful information.” The use of systems such as electronic data interchange (EDI) and XML messaging are being utilised by these companies to try and maintain information continuity, but even these system pose their own challenges such as being out of sync and moving data only one stop down the supply chain, “The result: inventory that seems to be in two places at once,” added Brody. “These systems were created for an era of big, vertically integrated companies with large, but mostly static supply chains.” Although relevant 30 years ago, in today's modern supply chain this is not the case. ... “Until the advent of bitcoin and blockchain technology, the only way you could get a large number of entities to agree upon a shared, truthful set of data, such as who has what bank balance, was to appoint an impartial intermediary to process and account for all transactions,” highlighted Brody.
Microsoft is suddenly recommending Google products
Not merely extensions, but great extensions. I'm tempted to suspect a lawyer may have written that. Or at least someone in the Google marketing department. Naturally, I asked Microsoft why it had suddenly lurched from prickly to cuddly. Could it be that Google and Microsoft had a kiss-and-make-up Zoom call -- I mean, a Microsoft Teams call? Or a Google Meet encounter? Microsoft declined to comment. Perhaps, you might think, Microsoft has stopped to play nice merely because that's its brand image these days. Or perhaps some Redmonder stopped to think that, indeed, Edge doesn't currently enjoy enough of its own extensions. My delvings into Redmond's innards suggest the latter may have driven the decision even more than the former. You really don't want to annoy your customers, do you? Especially when you can't currently offer them what they need. Of course, Edge is based on Google's Chromium platform. In my own experimentations, I've found it to be a more pleasant experience than Chrome. Just that little bit more responsive and generally brighter -- though I can't quite cope with Bing as my default search engine.
Expanding Data Governance into the Future
Recognition that good Data Governance has become a must has come none too soon. Donna Burbank, Managing Director at Global Data Strategy, notes that many companies are beginning or planning to begin a Data Governance program, including a broader range of industries than before. However, spreading an existing successful Data Governance framework in one business area does not necessarily translate across the entire enterprise, or even to another company. Freddie Mac tried several times to implement DG driven by IT, and nothing stuck until a next-generation proactive and collaborative Data Governance took hold. Unfortunately, many companies, like Freddie Mac, get stuck in old patterns, trying to evangelize rigid Data Governance practices, gumming up operations, and fostering mistrust. Firms in this situation, according to Derek Steer, CEO at Mode, end up governing the wrong amount of data (missing the highest priority data assets) or enforcing Data Governance poorly (spending too much or too little time maintaining Data Governance logic). The first steps include understanding lessons from initial DG processes, how DG has changed, and how the next generation works better to support the business.
Amazon Faces A New Opponent: Some Of Its Own Tech Employees
Tech employees are speaking out for their blue-collar counterparts partly because the warehouse workers asked them to. Costa, who had been at the company for 15 years before she was fired, says warehouse workers reached out in March to the Amazon Employees for Climate Justice (AECJ), an internal group she co-founded two years ago, for help and support during the pandemic. “Tech workers are ‘a valued resource,’” Costa says. “They [Amazon management] see us as less expendable than warehouse workers because they know they can’t just throw more bodies at our seats if we leave. We have more leverage, and that’s why tech workers have much more privilege and have that much more responsibility to speak out.” AECJ organized a one-hour video call in mid-April during which warehouse workers could speak to Amazon tech employees who were interested to hear from them directly. The invite was sent out via Amazon’s internal e-mail system on Friday, April 10. “It got 1,550 accepts on a Friday afternoon, when New York, Europe and India were already off the clock,” Costa said.
Quote for the day:
"Leadership without character is unthinkable - or should be." -- Warren Bennis
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