mobrec

So
'Enterprise Architect' is a very fashionable title these days which causes a bit of confusion (and consternation) for actual EA practitioners. Typically, this title is attached to the role of someone who has deep technical knowledge about a given technology/application/suite. This is not an Enterprise Architect.

This article does a great job of clarifying what Enterprise Architecture is and what an Architect does (or should do).

“Before answering that question, it is important to note that no architecture is a solution. Often people confuse a solution, such as corporate infrastructure, as the architecture. This is an all too common mistake and very misleading. An architecture guided the development of the infrastructure, the infrastructure is a solution – not the architecture.

“The architect’s role isn’t to create solutions. Rather the architect’s role is to inform decision-makers and guide development of solutions based on understanding business drivers and needs of the organization. Although a single person can have both a role as an architect and a developer. The architect typically takes a broader and material independent view than the developer, yet leaves much downstream detail to the development community.

“So, since architecture is not a solution what is it? It is a package of information that describes what is needed to achieve a given result and what it might look like in a future state if implemented. In order for an architecture to be effective, that is for it to be realized in solutions, it must guide decisions.

“Any good architecture addresses a well-defined scope and seeks to achieve specified goals. For example, an architecture for a back-office software suite will seek to enable improvements to back office operations, an architecture for a department network will enable department interconnectivity, an architecture for corporate infrastructure will address needed services throughout at lower costs, etc. For each scope there are decision-makers that can either accept or reject the guidance from the architect such as office managers, network managers, the head of IT, etc.

“Those that deal with Enterprise Architecture take the broadest view, deal with issues that are oftentimes beyond even the corporate level, and are most effective when they influence corporate or Board level decision-makers.

So
This post is a refreshing counterpoint to the breathless 'AI will take over everything' reporting that is increasingly common of late.

Self-driving cars The first area is that “we won’t be riding in self-driving cars”. As Dr. Reddy explains: “While many are predicting a driverless future, we’re a long 'road' away from autonomous vehicles.” This is is terms of cars that will take commuters to work, a situation where the commuter can sit back and read his or her iPad while paying little attention to the traffic outside. He adds: “For a number of years ahead, human operators and oversight will still rule the roads, because the discrete human judgments that are essential while driving will still require a person with all of his or her faculties — and the attendant liability for when mistakes happen. Besides technical challenges, humans tend to be more forgiving about mistakes made by human intelligence as opposed to those made by artificial intelligence.” Disappearing jobs The second ‘unprediction’ is that people will not be replaced by AI bots this year. Dr. Reddy states: “While it is possible that artificial intelligence agents might replace (but more likely supplement) certain administrative tasks, the reality is that worker displacement by AI is over-hyped and unlikely.” So robots won't be taking over most jobs any time soon. This is because, the analyst states: “Even in an environment where Automated Machine Learning is helping machines to build machines through deep learning, the really complex aspects of jobs will not be replaced. Thus, while AI will help automate various tasks that mostly we don’t want to do anyway, we’ll still need the human knowledge workers for thinking, judgment and creativity. But, routine tasks beware: AI is coming for you!” Medical diagnosis The third aspect is that we won’t get AI-powered medical diagnoses. This is, Dr. Reddy says “Due to a lack of training data and continued challenges around learning diagnosis and prognosis decision-making through identifying patterns, AI algorithms are not very good at medical decision automation and will only be used on a limited basis to support but not replace diagnosis and treatment recommendations by humans.” He adds: “AI will be increasingly deployed against sporadic research needs in the medical arena, but, as with fraud detection, pattern recognition by machines only goes so far, and human insight, ingenuity and judgment come into play. People are still better than machines at learning patterns and developing intuition about new approaches.” Importantly: “People are still better than machines at learning patterns and developing intuition about new approaches.” AI at work The fourth and final area is that we will still struggle with determining where artificial intelligence should be deployed. Dr. Reddy states: “Despite what you might be hearing from AI solution vendors, businesses that want to adopt AI must first conduct a careful needs assessment. As part of this process, companies also must gain a realistic view of what benefits are being sought and how AI can be strategically deployed for maximum benefit.” The analyst adds: “IT management, business users and developers should avoid being overly ambitious and carefully assess the infrastructure and data required to drive value from AI. Best practices and 'buy versus build' analysis also should be part of the conversations about implementing AI applications.”

So
This is an excellent TEDTalk on what is missing from bigdata (hint: it is the human element).

Why do so many companies make bad decisions, even with access to unprecedented amounts of data? With stories from Nokia to Netflix to the oracles of ancient Greece, Tricia Wang demystifies big data and identifies its pitfalls, suggesting that we focus instead on “thick data” — precious, unquantifiable insights from actual people — to make the right business decisions and thrive in the unknown.

An interesting (but not too surprising) stat from the intro is that 73% of all bigdata projects deliver no value.

So
Not one of the stronger PopSci articles I have seen. The 'forensics' consist mainly of 'look closely at the image and think about it'. Em, ok.

Oh, and, warning, there is about 20x more auto-start ads and video on the linked page than there is actual useful content.

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Really? Someone had to write a 500 word 'article' about what should be common sense?

There's a solution, though: Kill your notifications. Yes, really. Turn them all off. (You can leave on phone calls and text messages, if you must, but nothing else.) You'll discover that you don't miss the stream of cards filling your lockscreen, because they never existed for your benefit. They're for brands and developers, methods by which thirsty growth hackers can grab your attention anytime they want. Allowing an app to send you push notifications is like allowing a store clerk to grab you by the ear and drag you into their store. You're letting someone insert a commercial into your life anytime they want. Time to turn it off.

So
It seems like 'free' access to large datasets is the new giveaway/razor in the hopes that revenue will be generated by usage of the AI/analytics tools/razor blades that are co-hosted with the datasets.

So
This is an interesting overview of a Roomba-style robot that weeds your garden (rather than vacuuming you house) as well as a discussion of the challenges of designing autonomous gadgets for the consumer market.

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https://www.mcsweeneys.net/articles/welcome-to-our-startup-where-everyone-is-23-years-old-because-we-believe-old-people-are-visually-displeasing-and-out-of-ideas

This is hilarious because it is true. I've seen so many 'startups' spend a huge amount of money and effort trying to imitate the trappings of a startup rather than having original ideas and actually producing something. Here is a sample (more at the link above):

Hello, and welcome to our startup. We hope you’re enjoying your complimentary snifter of vaporized coconut water. Once you’re done, please place the glass into one of the blue receptacles around the office, which will send the glass to be washed and dried. Do not place it into one of the red receptacles. The red receptacles take whatever you put inside of them and launch it into space.

If you look to your left, you’ll see one of our employees using a state-of-the-art ergonomic sleeping desk. Most startups have standing desks, but we have sleeping desks, dancing desks, and even skateboarding desks. The skateboarding desks are just large skateboards you can use to skate around the office. Be careful while skating, though, because we don’t offer any sort of medical insurance, since our benefits budget all goes toward cool desks.

So
This week, Tesla saw its stock drop by around 13% percent this week. Some were quick to pin this on the Volvo EV announcement, but I think that was a small part of it. I think that the bigger part was the reduced collision rating that the Tesla S received this week as well as the market's seeming need to take down 'tall poppies' like Tesla as any/ever chance they get.

So
Google provides some clear and concise guidance on choosing the best compute platform on the Google Cloud Platform.