Technology Review (April 2004)

April 2004 news and reviews

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Artificial Intelligence. This is how the robot uprising finally begins. Tech Policy. Also in this issue. Future of Work The productivity paradox Why brilliant AI technologies are not leading to widespread growth and prosperity. Future of Work Confessions of an accidental job destroyer Behind every piece of automation is a human who made it happen. Science Fiction. Past Issues Year s s s s s s s s s s s s See Issue Download. Artificial intelligence is getting very good at identifying things: show it a million pictures, and it can tell you with uncanny accuracy which ones depict a pedestrian crossing a street.

But AI is hopeless at generating images of pedestrians by itself. If it could do that, it would be able to create gobs of realistic but synthetic pictures depicting pedestrians in various settings, which a self-driving car could use to train itself without ever going out on the road. The problem is, creating something entirely new requires imagination — and until now that has perplexed AIs.

The solution first occurred to Ian Goodfellow, then a PhD student at the University of Montreal, during an academic argument in a bar in The approach, known as a generative adversarial network, or GAN, takes two neural networks — the simplified mathematical models of the human brain that underpin most modern machine learning — and pits them against each other in a digital cat-and-mouse game.

Both networks are trained on the same data set. The second, known as the discriminator, is asked to identify whether the example it sees is like the images it has been trained on or a fake produced by the generator — basically, is that three-armed person likely to be real? Essentially, the generator has been taught to recognize, and then create, realistic-looking images of pedestrians. The technology has become one of the most promising advances in AI in the past decade, able to help machines produce results that fool even humans. GANs have been put to use creating realistic-sounding speech and photorealistic fake imagery.

Another research group made not-unconvincing fake paintings that look like the works of van Gogh. Pushed further, GANs can reimagine images in different ways — making a sunny road appear snowy, or turning horses into zebras.

And that means AI may gain, along with a sense of imagination, a more independent ability to make sense of what it sees in the world. These work with its Pixel smartphones and Google Translate app to produce practically real-time translation. One person wears the earbuds, while the other holds a phone. The earbud wearer speaks in his or her language — English is the default — and the app translates the talking and plays it aloud on the phone.

The person holding the phone responds; this response is translated and played through the earbuds. Pixel Buds get around these problems because the wearer taps and holds a finger on the right earbud while talking. They do look silly, and they may not fit well in your ears. They can also be hard to set up with a phone. Clunky hardware can be fixed, though. Pixel Buds show the promise of mutually intelligible communication between languages in close to real time.

Research Interests

And no fish required. The world is probably stuck with natural gas as one of our primary sources of electricity for the foreseeable future. A pilot power plant just outside Houston, in the heart of the US petroleum and refining industry, is testing a technology that could make clean energy from natural gas a reality.

The company behind the megawatt project, Net Power, believes it can generate power at least as cheaply as standard natural-gas plants and capture essentially all the carbon dioxide released in the process. If so, it would mean the world has a way to produce carbon-free energy from a fossil fuel at a reasonable cost. The company is in the process of commissioning the plant and has begun initial testing.

It intends to release results from early evaluations in the months ahead. Much of the carbon dioxide can be continuously recycled; the rest can be captured cheaply. A key part of pushing down the costs depends on selling that carbon dioxide. Today the main use is in helping to extract oil from petroleum wells. Eventually, however, Net Power hopes to see growing demand for carbon dioxide in cement manufacturing and in making plastics and other carbon-based materials.

That limits the risk of a privacy breach or identity theft.

Much of the credit for a practical zero-knowledge proof goes to Zcash, a digital currency that launched in late Though these transactions are theoretically anonymous, they can be combined with other data to track and even identify users. One day, babies will get DNA report cards at birth. These reports will offer predictions about their chances of suffering a heart attack or cancer, of getting hooked on tobacco, and of being smarter than average.

The science making these report cards possible has suddenly arrived, thanks to huge genetic studies — some involving more than a million people. It turns out that most common diseases and many behaviors and traits, including intelligence, are a result of not one or a few genes but many acting in concert.

Though the new DNA tests offer probabilities, not diagnoses, they could greatly benefit medicine. For example, if women at high risk for breast cancer got more mammograms and those at low risk got fewer, those exams might catch more real cancers and set off fewer false alarms. By picking volunteers who are more likely to get sick, they can more accurately test how well the drugs work.

The trouble is, the predictions are far from perfect.

Look for Role Models Outside Your Industry

What if someone with a low risk score for cancer puts off being screened, and then develops cancer anyway? Deals are easily explained to and understood by boards, shareholders, and the media. They offer the prospect of nearly immediate gratification, and the bold stroke of a deal is consistent with the modern image of the executive as someone who focuses on grand strategy and leaves operational details to others. The fact that the great majority of deals are unsuccessful does not deter executives from pursuing them. Finance and strategy are at the top, marketing and sales occupy the middle tier, and operations is at the bottom.

A journalist at a prominent business magazine, assigned to do a story on operations, confessed that he thought it boring. This is the state of our business culture. The core, value-creating work of enterprises has become low status. At its heart, operations is a branch of engineering. It requires a skill set and a mind-set different from those needed in most other executive activities.

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Many top managers are ignorant about operations and uninterested in learning more. They enter the organization through finance, strategy, or marketing and build their reputations on work in these domains. When they move into their first general management role, they rely on others—plant managers, engineers, customer service leaders—to mind the details of the actual work.

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At a major semiconductor maker, for instance, a group of middle managers who were frustrated with the complexity and poor performance of their order fulfillment process decided to make a case for change to executive management. They created a two-page diagram illustrating the endless series of steps every order went through, the redundant moves of the product between factories and depots, the accumulations of inventory, and the enormous delays.

It should not be surprising that executives without experience in operations do not look there for competitive advantage. The information they usually get does little to focus their attention on the mechanics of operations.

How many executives receive data about order fulfillment cycle time, 0r the accuracy of customer service responses, or the cost of each procurement transaction, or the percentage of parts that are reused in new products? Indeed, in how many organizations is such information available at all? Financial data dominate the discourse in the modern organization, although operational performance is the driver of financial results. No one holds the title Vice President of Operational Innovation; it is organizationally homeless.

Functional line managers are too focused on meeting deadlines to have time for or interest in inventing new ways of doing things.

Robot dexterity

Normal planning and budgeting focus on investments in new equipment, products, and services and take account of process improvement. No wonder operational innovation has a hard time gaining traction in an organization. This is particularly problematic because operational innovation can easily founder in a sea of competing but smaller change initiatives.

Julie Wosk

It is all too common for enterprises today to have dozens—even hundreds—of operational improvement programs under way at any point in time. Some are technologically based, such as the implementation of enterprise resource planning ERP , customer relationship management CRM , or supply chain management SCM software systems.

Others are centered on specific bodies of improvement techniques, such as Six Sigma quality or lean enterprise programs. Still others are defined in terms of outcomes, such as accelerating time to market or presenting a single face to customers, or focused on improving a particular aspect of the enterprise procurement or customer service, for example. Each project typically has a narrow scope, a group of experts dedicated to it, and a sponsor whose enthusiasm is tolerated by his or her peers only as long as it is kept within bounds.

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