Posts tagged technology
The Achilles Heel of Artificial Intelligence

An AI process can appear quite intelligent within very specific bounds yet fall apart if the context in which the process was built is changed. In this blog I will discuss why adding an awareness of context into an AI process – and dealing with that context – may prove to be the hardest part of succeeding with AI. In fact, handling context may be the Achille’s heel of AI!

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Don't Mistake a Legacy Skillset for a Legacy Mindset

It goes without saying that the analytics and data science space is undergoing change at an unprecedented pace. At the same time, many organizations have a lot of people who are still working in the same way using the same skills they have had for years. Organizations certainly need to move beyond those legacy skillsets and the need for a skills update is real. However, I see many organizations assuming that those with legacy skillsets are no longer valuable and must be replaced. This is a mistake. There is a huge difference between a legacy skillset and a legacy mindset. The two are not the same!

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Push Your Analytics Out to Customers

Analytics and big data have penetrated most large organizations by now, and are helping to improve many internal decisions. But they can also have a major impact on the decisions of customers or citizens. This applies not only to decisions about what products to buy, but also to decisions about safety and crime.

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Beyond the Black Box in Analytics and Cognitive

There is a growing crisis in the world of analytics and cognitive technologies, and as of yet there is no obvious solution. The crisis was created by a spate of good news in the field of cognitive technology algorithms: they’re working! Specifically, a relatively new and complex type of algorithms—deep learning neural networks (DLNN)—have been able to learn from lots of labeled data and accomplish a variety of tasks. They can master difficult games (Go, for example), recognize images, translate speech, and perform many more tasks as well as or better than the best humans.

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Six Very Clear Signs That Your Job is Due to be Automated

As intelligent technologies take over more and more of the decision-making territory once occupied by humans, are you taking any action? Are you sufficiently aware of the signs that you should? To help you get the head start you may need, here are the signs that it’s time to fly the nest. All of them are evidence that a knowledge worker’s job is on the path to automation.

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Just How Smart are Smart Machines?

The number of sophisticated cognitive technologies that might be capable of cutting into the need for human labor is expanding rapidly. But linking these offerings to an organization’s business needs requires a deep understanding of their capabilities. If popular culture is an accurate gauge of what’s on the public’s mind, it seems everyone has suddenly awakened to the threat of smart machines.

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How To Help Humans Work Better With Smart Machines

It’s pretty clear that smart machines—computers and robots that can digest information, make recommendations and decisions, and take informed actions—are going to be a significant factor in the workplace of the future. In some areas, like insurance underwriting, credit decisions, and financial trading, they’re already in wide use. In other areas such as medical diagnosis and treatment, document analysis in commercial litigation, and digital marketing, they’re taking hold rapidly.

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Cognitive Computing In Healthcare: Early Adopters Of IBM's Watson

IBM’s Watson is one of the most appealing new technologies of the 21st century, and the most prominent example of the new category of “cognitive computing.” It burst upon the scene with a dramatic Jeopardy! win in 2011, and has now been adopted by a variety of business and health care organizations since then. For several months I have been speaking with the firms and organizations that signed on to Watson deals at a relatively early stage. Since most of the earliest adopters of the technology were health care organizations, I’ll focus on that industry in this column.

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Meet Your Next Lawyer, The Algorithm

The legal profession has been one of the least aggressive adopters of technology in the past, and in many ways the field resembles the law as practiced a hundred years ago. But it’s on the verge of a major transformation involving automation and the use of technology to make intelligent legal decisions. The legal profession, already suffering from an excess of supply over demand, could be decimated unless lawyers embrace smart machines much more than in the past.

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Every time I speak at a conference or on a webcast, one of the most frequent questions involves the “best” way to organize analytical and big data activity within a large organization. Should the function be centralized or decentralized? Should analysts and data scientists be attached to business functions and units, or in a central pool? To which existing function or organization should it report?

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Will Human Analysts Ever Go Away?

Once a week or so I hear from vendors who are creating the “data scientist in a box.” They say they can use software and hardware to get rid of those pesky human data scientists. Somewhat less frequently I hear from senior managers that they want to pursue analytics without analysts. One online travel website CEO heard me speak, and told me afterward, “I like what you say about analytics. But at my company we are going to do it without analysts—using machine learning instead.”

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Taming Big Data Is Not A Technical Issue

One thing that has struck me recently is that most of the focus when discussing big data is upon the technologies involved. The consensus seems to be that the biggest challenge with big data is a technological one, yet I don’t believe this to be the case. Sure, there are challenges today for organizations using big data, but, I would like to submit to you that technology is not the biggest problem. In fact, technology may be one of the easiest problems to solve when it comes time to tame big data.

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What's The Definition Of Big Data? Who Cares?

It has been entertaining to see how so many people are arguing over how to define big data. There is always another nuance that can be suggested. There is always another potential exception to any rule that is offered. In the end, I don’t think the energy being put into the discussions is of much tangible value from a business perspective versus really just being an academic exercise.  Let’s explore why.

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