Category: Disruptive Technology

If your ambition is to fly into space – and you’ve got plenty of spare cash – then 2020 could be an exciting year.

If space travel is not really your thing, but you would like a much bigger screen on your mobile phone, then 2020 might also have some tech for you.

But if you think there are already too many phones out there and the technology industry needs to be less wasteful, well some tech companies might catch up with your thinking.

Here’s a little taster of what might be coming in the next twelve months.

Crewed space missions

2020 is going to be a “pivotal year” for space travel, according to Guy Norris, a senior editor at Aviation Week & Space Technology.

Since Nasa retired the Space Shuttle in 2011, the US has relied on Russian spacecraft to transport astronauts to the International Space Station.

That could all change in 2020 when, if all goes to plan, two US-built spacecraft should start carrying crew.

Boeing’s CST-100 Starliner, which can carry up to seven astronauts into orbit, is due for its first test flight today before the first manned flight, likely to be in 2020.

Meanwhile the SpaceX Dragon capsule will go through some final tests in early 2020, and if they all go well then it too would be ready for a crewed mission.

Other systems, designed to reach near-Earth space, could also reach milestones in 2020. Blue Origin, owned by Amazon billionaire Jeff Bezos, could be ready to take tourists on its New Shepard suborbital rocket.

Virgin Galactic could also be ready in 2020 to take passengers into space, more than a decade later than founder Richard Branson originally hoped.

It’s reported that more than 600 people have put down deposits for a Virgin Galactic flight, with tickets costing $250,000 (£195,000).

“It’s finally delivery time for a lot of these long promised programmes and a chance for a whole range of technologies to really prove themselves for the first time,” says Mr Norris.

Technology and the environment

Protests by Extinction Rebellion have helped move climate change up the agenda for technology companies.

Among those that will be under pressure are mobile phone makers. It’s estimated there are 18 billion phones lying around unused worldwide. With around 1.3 billion phones sold in 2019, that number is growing all the time.

Mobile phone makers will be under pressure to make their production processes greener and their phones more easily repairable.

The same will go for the makers of other consumer goods including TVs, washing machines and vacuum cleaners.

Also watch the companies that provide mobile phone services. Vodafone has already promised that in the UK by 2023 its networks will all run on sustainable energy sources. Others are likely to follow suit.

Business travel is under pressure as well. Ben Wood, an analyst at CCS Insight says it will become “socially unacceptable” to fly around the world for meetings and firms will switch to virtual meetings.

There could also be green initiatives from the cloud computing industry as well. Their facilities which house thousands of computer servers use huge amounts of power.

Flexible displays

The launch of Samsung’s first foldable phone in April did not go smoothly. Several reviewers broke the screens and the company had to make some rapid improvements before it went on sale in September.

Motorola had a more successful launch of its new Razr, although some reviewers complained about the price. But this is unlikely to hold the market back. Samsung is expected to launch other devices with flexible displays next year – possibly a tablet.

TCL, the second biggest maker of TVs in China, has also promised to launch its first mobile foldable device in 2020 and then other products quickly after that.

It is betting big on the market, having invested $5.5bn in developing flexible displays.

Analysts say that screens will be incorporated into all sorts of surfaces. Smart speakers might have wrap-around displays, watch-like devices will have straps with displays and fridge doors might have large screens.

Super-fast mobile

We can expect the rollout of high-speed mobile phone networks to continue. By the end of 2019 around 40 networks in 22 countries were offering 5G service.

By the end of 2020 that number will have more than doubled to to around 125 operators, says Kester Mann at CCS Insight.

“There could be an interesting development in the way 5G contracts are priced. A 5G contract without a phone will cost around £30 a month and for that you’re likely to get unlimited amounts of data.”

But analysts say that next year we may see prices based on the speed of the service you want – a bit like the way home broadband is already priced.

Vodafone is already offering contracts based on speed in the UK. Also in the UK, the network 3 is likely to push its 5G offering as an alternative to broadband at home, analysts say. That might appeal to people who move around a lot – students for example – and don’t want a fixed line service.

Quantum computing

Will next year be another big one for quantum computing; the technology which exploits the baffling but powerful behaviour of tiny particles such as electrons and photons?

In October Google said that its quantum computer had performed a task in 200 seconds, that the fastest supercomputer would have taken 10,000 years to complete. There was some quibbling over its achievement, but experts say it was a big moment.

“It’s a fantastic milestone,” says Philipp Gerbert, a member of the deep tech group at consultancy firm BCG: “It’s clear they exceeded the classical computer, by what margin you can debate. They disproved some lingering doubts.”

Mr Gerbert thinks other leaders in the field – IBM, Rigetti and IonQ – could also clear that hurdle: “They all have excellent teams, one or two will reach a similar stage over the next year.”

Once the technology is proven, quantum computers could spur breakthroughs in chemistry, pharmaceuticals and engineering.

Google has also promised to make its quantum computer available for use by outsiders in 2020, but has not provided any details yet.

“Clearly people would love to get access to that,” Mr Gerbert says.

Source: BBC

Will we start the journey to a better, kinder internet? Which countries are best placed to win the AI race? And should Ivanka Trump be speaking at a tech show? Just some of the questions we address in the first edition of Tech Tent this year.

Last month, the creator of the World Wide Web Sir Tim Berners-Lee, told us of his plan to put it back on the right track. His Contract for the Web aims to get companies, countries and individuals to work together to combat cyber-bullying, misinformation and other online harms.

Catherine Miller of the think tank dot everyone, which describes its mission as championing responsible technology for a fairer future, gives us her assessment of how likely it is that we will make the web a better place in 2020. She stresses that better regulation will be key, changing the economic incentives that mean the tech giants fight to keep people hooked to their platforms, and reward damaging behaviour.

When it comes to the race to build what is arguably the key technology of our times – artificial intelligence – the consensus has been that the United States is in the lead, but China is catching up fast. Now a new global AI index produced by the online news site Tortoise has come up with a more nuanced picture.

It found that, yes, the US and China were one and two in AI, with the UK in third place. But Alexandra Mousavizadeh, the data scientist who led the project for Tortoise Intelligence, tells us that China was much further behind than they had expected.

It scored well in research and development, but its 18th position in having the people with the right skills held it back. “This race is going to be won in many different ways,” says Ms Mousavizadeh, stressing that the free market bottom-up approach of the US had proved very fruitful so far, but the top-down Chinese strategy also has its strengths.

But she says that around the world a government strategy for developing human capital – “preparing a workforce for working with and being part of AI driven growth” – will be key.

We also look less far ahead – to CES, the huge annual gadget-fest which opens in Las Vegas on Tuesday. No doubt we will see all sorts of products promising to use AI to give consumers better experiences.

But one of the keynote speakers looks likely to provide the biggest headlines from the show. On the opening day, Ivanka Trump will be discussing the future of work in a session with the Consumer Technology Association’s CEO Gary Shapiro. The invitation to the President’s daughter has sparked controversy, especially as female keynote speakers from the tech industry have been thin on the ground in previous years.

Mr Shapiro tells Tech Tent that the show is about more than gadgets. It addresses key issues such as the impact of automation on work – and he says as the co-chair of the American Workforce Advisory Board, Ms Trump has significant things to contribute to this debate.

But back to technology. I have just been looking back at a blogpost I wrote on New Year’s Eve 2009 as I prepared to head off to the 2010 CES in Las Vegas.

I was very excited about a British firm called Plastic Logic that was going to unveil a radical new e-reader. “It could be one of the show’s stand-out products,” I wrote, “or it could end up buried under an avalanche of hype about a forthcoming rival device from a better-known firm.”

That rival device turned out to be Apple’s iPad, unveiled later that month, and Plastic Logic’s Que device did indeed end up dead and buried.

So, expect to see some startling new products emerging from Las Vegas in the next few days – we are promised a talking frying-pan and a self-driving sofa – but world-changing devices are few and far between, and are likely to be unveiled elsewhere.

Source: BBC

Every parent worries about the influences our children are exposed to. Who are their teachers? What movies are they watching? What video games are they playing? Are they hanging out with the right crowd? We scrutinize these influences because we know they can affect, for better or worse, the decisions our children make.

Just as we concern ourselves with who’s teaching our children, we also need to pay attention to who’s teaching our algorithms. Like humans, artificial intelligence systems learn from the environments they are exposed to and make decisions based on biases they develop. And like our children, we should expect our models to be able to explain their decisions as they develop.

As Cathy O’Neil explains in Weapons of Math Destruction, algorithms often determine what college we attend, if we get hired for a job, if we qualify for a loan to buy a house, and even who goes to prison and for how long. Unlike human decisions, these mathematical models are rarely questioned. They just show up on somebody’s computer screen and fates are determined.

In some cases, the errors of algorithms are obvious, such as when Dow Jones reported that Google was buying Apple for $9 billion and the bots fell for it or when Microsoft’s Tay chatbot went berserk on Twitter — but often they are not. What’s far more insidious and pervasive are the more subtle glitches that go unnoticed, but have very real effects on people’s lives.

Once you get on the wrong side of an algorithm, your life immediately becomes more difficult. Unable to get into a good school or to get a job, you earn less money and live in a worse neighborhood. Those facts get fed into new algorithms and your situation degrades even further. Each step of your descent is documented, measured, and evaluated.

Consider the case of Sarah Wysocki, a fifth grade teacher who — despite being lauded by parents, students, and administrators alike — was fired from the D.C. school district because an algorithm judged her performance to be sub-par. Why? It’s not exactly clear, because the system was too complex to be understood by those who fired her.

Make no mistake, as we increasingly outsource decisions to algorithms, the problem has the potential to become even more Kafkaesque. It is imperative that we begin to take the problem of AI bias seriously and take steps to mitigate its effects by making our systems more transparent, explainable, and auditable.

Sources of Bias

Bias in AI systems has two major sources: the data sets on which models are trained, and the design of the models themselves. Biases in the data sets on which algorithms are trained can be subtle, for example, such as when smartphone apps are used to monitor potholes and alert authorities to contact maintenance crews. That may be efficient, but it’s bound to undercount poorer areas where fewer people have smartphones.

In other cases, data that is not collected can affect results. Analysts suspect that’s what happened when Google Flu Trends predicted almost double as many cases in 2013 as there actually were. What appears to have happened is that increased media coverage led to more searches by people who weren’t sick.

Yet another source of data bias happens when human biases carry over into AI systems. For example, biases in the judicial system affect who gets charged and sentenced for crimes. If that data is then used to predict who is likely to commit crimes, then those biases will carry over. In other cases, humans are used to tag data and may direct input bias into the system.

This type of bias is pervasive and difficult to eliminate. In fact, Amazon was forced to scrap an AI-powered recruiting tool because they could not remove gender bias from the results. They were unfairly favoring men because the training data they used taught the system that most of the previously-hired employees of the firm that were viewed as successful were male. Even when they eliminated any specific mention of gender, certain words which appeared more often in male resumes than female resumes were identified by the system as proxies for gender.

A second major source of bias results from how decision-making models are designed. For example, if a teacher’s ability is evaluated based on test scores, then other aspects of performance, such as taking on children with learning differences or emotional problems, would fail to register, or even unfairly penalize them. In other cases, models are constructed according to what data is easiest to acquire or the model is overfit to a specific set of cases and is then applied too broadly.

Overcoming Bias

With so many diverse sources of bias, we do not think it is realistic to believe we can eliminate it entirely, or even substantially. However, what we can do is make our AI systems more explainable, auditable, and transparent. We suggest three practical steps leaders can take to mitigate the effects of bias.

First, AI systems must be subjected to vigorous human review. For example, one study cited by a White House report during the Obama administration found that while machines had a 7.5% error rate in reading radiology images, and humans had a 3.5% error rate, when humans combined their work with machines the error rate dropped to 0.5%.

Second, much like banks are required by law to “know their customer,” engineers that build systems need to know their algorithms. For example, Eric Haller, head of Datalabs at Experian told us that unlike decades ago, when the models they used were fairly simple, in the AI era, his data scientists need to be much more careful. “In the past, we just needed to keep accurate records so that, if a mistake was made, we could go back, find the problem and fix it,” he told us. “Now, when so many of our models are powered by artificial intelligence, it’s not so easy. We can’t just download open-source code and run it. We need to understand, on a very deep level, every line of code that goes into our algorithms and be able to explain it to external stakeholders.”

Third, AI systems, and the data sources used to train them, need to be transparent and available for audit. Legislative frameworks like GDPR in Europe have made some promising first steps, but clearly more work needs to be done. We wouldn’t find it acceptable for humans to be making decisions without any oversight, so there’s no reason why we should accept it when machines make decisions.

Perhaps most of all, we need to shift from a culture of automation to augmentation. Artificial intelligence works best not as some sort of magic box you use to replace humans and cut costs, but as a force multiplier that you use to create new value. By making AI more explainable, auditable and transparent, we can not only make our systems more fair, we can make them vastly more effective and more useful.

Source: https://hbr.org/2019/10/we-need-ai-that-is-explainable-auditable-and-transparent

Don’t fear the robots, according to a report from MIT and IBM. Worry about algorithms replacing any task that can be automated. 

Martin Fleming doesn’t think robots are coming to take your jobs. The chief economist at IBM, Fleming says those worries aren’t backed up by the data. “It’s really nonsense,” he says. A new paper from MIT and IBM’s Watson AI Lab shows that for most of us, the automation revolution probably won’t mean physical robots replacing human workers. Instead, it will come from algorithms. And while we won’t all lose our jobs, those jobs will change, thanks to artificial intelligence and machine learning.

Fleming and a team of researchers analyzed 170 million online US job listings, collected by the job analytics firm Burning Glass Technologies, that were posted between 2010 and 2017. They found that, on average, tasks such as scheduling or credential validation, which could be performed by AI, appeared less frequently in the job listings in the more recent years. The recent listings also included more “soft skills” requirements like creativity, common sense, and judgment. Fleming says this shows that work is being resorted. AI is taking over more easily automated tasks and workers are being asked to do things that machines can’t do.

If you’re in sales, for example, you’ll spend less time figuring out the ideal price for your product, because an algorithm can determine the optimal price to maximize profits. Instead, you might spend more time managing customers or designing attractive marketing materials or websites.

In the study, researchers divided the listings into three groups based on the advertised pay, then examined how different tasks were being valued. What they found is that how we value tasks may be starting to change.

Design skills, for example, were in particularly high demand and increased the most across wage brackets. Within personal care and services occupations—which generally are low-wage—pay for jobs that included design tasks, such as presentation design or digital design, increased by an average of $12,000 over the study period, after inflation. The same can be said of higher wage earners in business and finance who have deep industry expertise that can’t yet be matched by AI. Their wages went up more than $6,000 annually.

Some low-wage occupations like home health care, hairstyling, or fitness training are insulated from the impact of AI because those skills are hard to automate. But middle-wage earners are starting to feel the squeeze. Their wages are still rising, but after adjusting for the shifts in tasks for those jobs, the report found, those wages weren’t growing as quickly as low-wage and high-wage jobs. In some industries, like manufacturing and production, wages actually decreased. There are also fewer middle-wage jobs. Some are getting simpler and being replaced by low-wage jobs. Others now require more skills and are becoming high wage.

Fleming is optimistic about what AI tools can do for work and for workers. Just as automation made factories more efficient, AI can help white-collar workers be more productive. The more productive they are, the more value they add to their companies. And the better those companies do, the higher wages get. “There will be some jobs lost,” he says. “But on balance, more jobs will be created both in the US and worldwide.” While some middle-wage jobs are disappearing, others are popping up in industries like logistics and health care, he says.

As AI starts to take over more tasks, and the middle-wage jobs start to change, the skills we associate with those middle-class jobs have to change too. “I think that it’s rational to be optimistic,” says Richard Reeves, director of the Future of the Middle Class Initiative at the Brookings Institution. “But I don’t think that we should be complacent. It won’t just automatically be OK.”

The report says these changes are happening relatively slowly, giving workers time to adjust. But Reeves points out that while these changes may seem incremental now, they are happening faster than they used to. AI has been an academic project since the 1950s. It remained a niche concept until 2012, when tests showed neural networks could make speech and image recognition more accurate. Now we use it to complete emails, analyze surveillance footage, and decide prison sentencing. The IBM and MIT researchers used it to help sort through all the data they analyzed for this paper.

That fast adoption means that workers are watching their jobs change. We need a way to help people adjust from the jobs they used to have to the jobs that are now available. “Our optimism actually is rather contingent on our actions, on actually making good on our promise to reskill,” says Reeves. “We are rewiring our economy but we haven’t rewired our training and education programs.”

Read more: https://www.wired.com/story/ai-not-kill-job-change-it/

Last year, many Americans worried that artificial intelligence (AI) might replace them at work. This year, employees around the world are wondering why their employers don’t provide them with the kind of AI-enabled technology they’re starting to use at home. 

That’s one way to think about the results of a second annual survey about AI in the workplace, conducted by Oracle and research firm Future Workplace. This year, 50% of survey respondents say they’re currently using some form of AI at work—a major leap compared to only 32% in last year’s survey.

Last year, many Americans worried that artificial intelligence (AI) might replace them at work. This year, employees around the world are wondering why their employers don’t provide them with the kind of AI-enabled technology they’re starting to use at home. 

That’s one way to think about the results of a second annual survey about AI in the workplace, conducted by Oracle and research firm Future Workplace. This year, 50% of survey respondents say they’re currently using some form of AI at work—a major leap compared to only 32% in last year’s survey.

Source: https://www.forbes.com/sites/oracle/2019/10/31/employees-worldwide-welcome-ai-coworkers-to-the-office/#47aa68266681

Thinking With Portals

Scientists think they’ve come up with a way to detect traversable wormholes, assuming they exist.

There’s never been any sort of evidence that traversable wormholes — portals between two distant parts of the universe, or two universes within a theoretical multiverse — are real. But if they are, a team of scientists think they know what that evidence might look like, breathing new life into a far-out theory that could finally achieve faster-than-light travel.

Telltale Wobbles

If a wormhole were to exist, then the gravitational pull of objects on one side, like black holes or stars, would influence the objects on the other side.

If a star wobbled or had otherwise inexplicable perturbations in its orbit around a black hole, researchers could hypothetically argue that they’re being influenced by the gravity of something on the other end of a wormhole, according to research published this month in the journal Physical Review D.

“If you have two stars, one on each side of the wormhole, the star on our side should feel the gravitational influence of the star that’s on the other side,” said University at Buffalo physicist Dejan Stojkovic. “The gravitational flux will go through the wormhole.”

Occam’s Razor

While the researchers hope to look for wobbles in the orbits of stars orbiting near Sagittarius A*, the supermassive black hole at the center of the Milky Way, Stojkovic concedes that spotting some wouldn’t guarantee that a wormhole exists there.

Brain-computer interfaces could make VR gaming way more immersive.

Mind Control
Virtual reality headsets are already pretty good at fooling our eyes and ears into thinking we’re in another world. And soon, we might be able to navigate that world with our thoughts alone.

Speaking at this year’s Game Developer’s Conference in San Francisco, Mike Abinder, in-house psychologist and researcher for game developer and distributor Valve, gave a talk on the exciting possibilities of adding brain-computer interfaces to VR headsets.

Personalized Gaming
The idea is to add non-invasive electroencephalogram (EEG) sensors to the insides of existing VR headsets. EEG readers detect the electrical signals firing in the brain and turn them into data points. And by analyzing that data, according to Abinder, game designers could make games that respond differently depending on whether you’re excited, happy, sad or bored.

“So think about adaptive enemies. What kinds of animals do you like playing against in gaming?” Ambinder said, as quoted by VentureBeat. “If we knew the answers to these questions, you could have the game give you more of a challenging time and less of the boring time.”

Game design could become almost perfectly tailored to the person wearing the VR headset — or even recreate a perfect representation of you inside a virtual world. Your avatar could perfectly mimic your current state of mind or mood.

“All of a sudden, we start becoming able to assess how you’re responding to various elements in game,” Ambinder continued. “We can make small changes to make big changes.”

Brain Extensions
There are a handful of companies already trying to harness brain signals for enhancing gaming experiences. A startup called Neurable is already testing out BCIs built into off-the-shelf VR headsets “to create a natural extension of our brains, creating new possibilities for human empowerment,” according to its website.

Of course, Abinder’s vision of the future of gaming is mostly a fun thought experiment at this stage. Even hospital-grade EEGs have to deal with a huge amount of noise — and that’s especially the case for consumer-grade, non-invasive scanners that are not planted to the scalp or surgically implanted.

Source: https://futurism.com/brain-computer-interface-vr-headsets