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#432880 Google’s Duplex Raises the Question: ...

By now, you’ve probably seen Google’s new Duplex software, which promises to call people on your behalf to book appointments for haircuts and the like. As yet, it only exists in demo form, but already it seems like Google has made a big stride towards capturing a market that plenty of companies have had their eye on for quite some time. This software is impressive, but it raises questions.

Many of you will be familiar with the stilted, robotic conversations you can have with early chatbots that are, essentially, glorified menus. Instead of pressing 1 to confirm or 2 to re-enter, some of these bots would allow for simple commands like “Yes” or “No,” replacing the buttons with limited ability to recognize a few words. Using them was often a far more frustrating experience than attempting to use a menu—there are few things more irritating than a robot saying, “Sorry, your response was not recognized.”

Google Duplex scheduling a hair salon appointment:

Google Duplex calling a restaurant:

Even getting the response recognized is hard enough. After all, there are countless different nuances and accents to baffle voice recognition software, and endless turns of phrase that amount to saying the same thing that can confound natural language processing (NLP), especially if you like your phrasing quirky.

You may think that standard customer-service type conversations all travel the same route, using similar words and phrasing. But when there are over 80,000 ways to order coffee, and making a mistake is frowned upon, even simple tasks require high accuracy over a huge dataset.

Advances in audio processing, neural networks, and NLP, as well as raw computing power, have meant that basic recognition of what someone is trying to say is less of an issue. Soundhound’s virtual assistant prides itself on being able to process complicated requests (perhaps needlessly complicated).

The deeper issue, as with all attempts to develop conversational machines, is one of understanding context. There are so many ways a conversation can go that attempting to construct a conversation two or three layers deep quickly runs into problems. Multiply the thousands of things people might say by the thousands they might say next, and the combinatorics of the challenge runs away from most chatbots, leaving them as either glorified menus, gimmicks, or rather bizarre to talk to.

Yet Google, who surely remembers from Glass the risk of premature debuts for technology, especially the kind that ask you to rethink how you interact with or trust in software, must have faith in Duplex to show it on the world stage. We know that startups like Semantic Machines and x.ai have received serious funding to perform very similar functions, using natural-language conversations to perform computing tasks, schedule meetings, book hotels, or purchase items.

It’s no great leap to imagine Google will soon do the same, bringing us closer to a world of onboard computing, where Lens labels the world around us and their assistant arranges it for us (all the while gathering more and more data it can convert into personalized ads). The early demos showed some clever tricks for keeping the conversation within a fairly narrow realm where the AI should be comfortable and competent, and the blog post that accompanied the release shows just how much effort has gone into the technology.

Yet given the privacy and ethics funk the tech industry finds itself in, and people’s general unease about AI, the main reaction to Duplex’s impressive demo was concern. The voice sounded too natural, bringing to mind Lyrebird and their warnings of deepfakes. You might trust “Do the Right Thing” Google with this technology, but it could usher in an era when automated robo-callers are far more convincing.

A more human-like voice may sound like a perfectly innocuous improvement, but the fact that the assistant interjects naturalistic “umm” and “mm-hm” responses to more perfectly mimic a human rubbed a lot of people the wrong way. This wasn’t just a voice assistant trying to sound less grinding and robotic; it was actively trying to deceive people into thinking they were talking to a human.

Google is running the risk of trying to get to conversational AI by going straight through the uncanny valley.

“Google’s experiments do appear to have been designed to deceive,” said Dr. Thomas King of the Oxford Internet Institute’s Digital Ethics Lab, according to Techcrunch. “Their main hypothesis was ‘can you distinguish this from a real person?’ In this case it’s unclear why their hypothesis was about deception and not the user experience… there should be some kind of mechanism there to let people know what it is they are speaking to.”

From Google’s perspective, being able to say “90 percent of callers can’t tell the difference between this and a human personal assistant” is an excellent marketing ploy, even though statistics about how many interactions are successful might be more relevant.

In fact, Duplex runs contrary to pretty much every major recommendation about ethics for the use of robotics or artificial intelligence, not to mention certain eavesdropping laws. Transparency is key to holding machines (and the people who design them) accountable, especially when it comes to decision-making.

Then there are the more subtle social issues. One prominent effect social media has had is to allow people to silo themselves; in echo chambers of like-minded individuals, it’s hard to see how other opinions exist. Technology exacerbates this by removing the evolutionary cues that go along with face-to-face interaction. Confronted with a pair of human eyes, people are more generous. Confronted with a Twitter avatar or a Facebook interface, people hurl abuse and criticism they’d never dream of using in a public setting.

Now that we can use technology to interact with ever fewer people, will it change us? Is it fair to offload the burden of dealing with a robot onto the poor human at the other end of the line, who might have to deal with dozens of such calls a day? Google has said that if the AI is in trouble, it will put you through to a human, which might help save receptionists from the hell of trying to explain a concept to dozens of dumbfounded AI assistants all day. But there’s always the risk that failures will be blamed on the person and not the machine.

As AI advances, could we end up treating the dwindling number of people in these “customer-facing” roles as the buggiest part of a fully automatic service? Will people start accusing each other of being robots on the phone, as well as on Twitter?

Google has provided plenty of reassurances about how the system will be used. They have said they will ensure that the system is identified, and it’s hardly difficult to resolve this problem; a slight change in the script from their demo would do it. For now, consumers will likely appreciate moves that make it clear whether the “intelligent agents” that make major decisions for us, that we interact with daily, and that hide behind social media avatars or phone numbers are real or artificial.

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#432878 Chinese Port Goes Full Robot With ...

By the end of 2018, something will be very different about the harbor area in the northern Chinese city of Caofeidian. If you were to visit, the whirring cranes and tractors driving containers to and fro would be the only things in sight.

Caofeidian is set to become the world’s first fully autonomous harbor by the end of the year. The US-Chinese startup TuSimple, a specialist in developing self-driving trucks, will replace human-driven terminal tractor-trucks with 20 self-driving models. A separate company handles crane automation, and a central control system will coordinate the movements of both.

According to Robert Brown, Director of Public Affairs at TuSimple, the project could quickly transform into a much wider trend. “The potential for automating systems in harbors and ports is staggering when considering the number of deep-water and inland ports around the world. At the same time, the closed, controlled nature of a port environment makes it a perfect proving ground for autonomous truck technology,” he said.

Going Global
The autonomous cranes and trucks have a big task ahead of them. Caofeidian currently processes around 300,000 TEU containers a year. Even if you were dealing with Lego bricks, that number of units would get you a decent-sized cathedral or a 22-foot-long aircraft carrier. For any maritime fans—or people who enjoy the moving of heavy objects—TEU stands for twenty-foot equivalent unit. It is the industry standard for containers. A TEU equals an 8-foot (2.43 meter) wide, 8.5-foot (2.59 meter) high, and 20-foot (6.06 meter) long container.

While impressive, the Caofeidian number pales in comparison with the biggest global ports like Shanghai, Singapore, Busan, or Rotterdam. For example, 2017 saw more than 40 million TEU moved through Shanghai port facilities.

Self-driving container vehicles have been trialled elsewhere, including in Yangshan, close to Shanghai, and Rotterdam. Qingdao New Qianwan Container Terminal in China recently laid claim to being the first fully automated terminal in Asia.

The potential for efficiencies has many ports interested in automation. Qingdao said its systems allow the terminal to operate in complete darkness and have reduced labor costs by 70 percent while increasing efficiency by 30 percent. In some cases, the number of workers needed to unload a cargo ship has gone from 60 to 9.

TuSimple says it is in negotiations with several other ports and also sees potential in related logistics-heavy fields.

Stable Testing Ground
For autonomous vehicles, ports seem like a perfect testing ground. They are restricted, confined areas with few to no pedestrians where operating speeds are limited. The predictability makes it unlike, say, city driving.

Robert Brown describes it as an ideal setting for the first adaptation of TuSimple’s technology. The company, which, amongst others, is backed by chipmaker Nvidia, have been retrofitting existing vehicles from Shaanxi Automobile Group with sensors and technology.

At the same time, it is running open road tests in Arizona and China of its Class 8 Level 4 autonomous trucks.

The Camera Approach
Dozens of autonomous truck startups are reported to have launched in China over the past two years. In other countries the situation is much the same, as the race for the future of goods transportation heats up. Startup companies like Embark, Einride, Starsky Robotics, and Drive.ai are just a few of the names in the space. They are facing competition from the likes of Tesla, Daimler, VW, Uber’s Otto subsidiary, and in March, Waymo announced it too was getting into the truck race.

Compared to many of its competitors, TuSimple’s autonomous driving system is based on a different approach. Instead of laser-based radar (LIDAR), TuSimple primarily uses cameras to gather data about its surroundings. Currently, the company uses ten cameras, including forward-facing, backward-facing, and wide-lens. Together, they produce the 360-degree “God View” of the vehicle’s surroundings, which is interpreted by the onboard autonomous driving systems.

Each camera gathers information at 30 frames a second. Millimeter wave radar is used as a secondary sensor. In total, the vehicles generate what Robert Brown describes with a laugh as “almost too much” data about its surroundings and is accurate beyond 300 meters in locating and identifying objects. This includes objects that have given LIDAR problems, such as black vehicles.

Another advantage is price. Companies often loathe revealing exact amounts, but Tesla has gone as far as to say that the ‘expected’ price of its autonomous truck will be from $150,0000 and upwards. While unconfirmed, TuSimple’s retrofitted, camera-based solution is thought to cost around $20,000.

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#432487 Can We Make a Musical Turing Test?

As artificial intelligence advances, we’re encountering the same old questions. How much of what we consider to be fundamentally human can be reduced to an algorithm? Can we create something sufficiently advanced that people can no longer distinguish between the two? This, after all, is the idea behind the Turing Test, which has yet to be passed.

At first glance, you might think music is beyond the realm of algorithms. Birds can sing, and people can compose symphonies. Music is evocative; it makes us feel. Very often, our intense personal and emotional attachments to music are because it reminds us of our shared humanity. We are told that creative jobs are the least likely to be automated. Creativity seems fundamentally human.

But I think above all, we view it as reductionist sacrilege: to dissect beautiful things. “If you try to strangle a skylark / to cut it up, see how it works / you will stop its heart from beating / you will stop its mouth from singing.” A human musician wrote that; a machine might be able to string words together that are happy or sad; it might even be able to conjure up a decent metaphor from the depths of some neural network—but could it understand humanity enough to produce art that speaks to humans?

Then, of course, there’s the other side of the debate. Music, after all, has a deeply mathematical structure; you can train a machine to produce harmonics. “In the teachings of Pythagoras and his followers, music was inseparable from numbers, which were thought to be the key to the whole spiritual and physical universe,” according to Grout in A History of Western Music. You might argue that the process of musical composition cannot be reduced to a simple algorithm, yet musicians have often done so. Mozart, with his “Dice Music,” used the roll of a dice to decide how to order musical fragments; creativity through an 18th-century random number generator. Algorithmic music goes back a very long way, with the first papers on the subject from the 1960s.

Then there’s the techno-enthusiast side of the argument. iTunes has 26 million songs, easily more than a century of music. A human could never listen to and learn from them all, but a machine could. It could also memorize every note of Beethoven. Music can be converted into MIDI files, a nice chewable data format that allows even a character-by-character neural net you can run on your computer to generate music. (Seriously, even I could get this thing working.)

Indeed, generating music in the style of Bach has long been a test for AI, and you can see neural networks gradually learn to imitate classical composers while trying to avoid overfitting. When an algorithm overfits, it essentially starts copying the existing music, rather than being inspired by it but creating something similar: a tightrope the best human artists learn to walk. Creativity doesn’t spring from nowhere; even maverick musical geniuses have their influences.

Does a machine have to be truly ‘creative’ to produce something that someone would find valuable? To what extent would listeners’ attitudes change if they thought they were hearing a human vs. an AI composition? This all suggests a musical Turing Test. Of course, it already exists. In fact, it’s run out of Dartmouth, the school that hosted that first, seminal AI summer conference. This year, the contest is bigger than ever: alongside the PoetiX, LimeriX and LyriX competitions for poetry and lyrics, there’s a DigiKidLit competition for children’s literature (although you may have reservations about exposing your children to neural-net generated content… it can get a bit surreal).

There’s also a pair of musical competitions, including one for original compositions in different genres. Key genres and styles are represented by Charlie Parker for Jazz and the Bach chorales for classical music. There’s also a free composition, and a contest where a human and an AI try to improvise together—the AI must respond to a human spontaneously, in real time, and in a musically pleasing way. Quite a challenge! In all cases, if any of the generated work is indistinguishable from human performers, the neural net has passed the Turing Test.

Did they? Here’s part of 2017’s winning sonnet from Charese Smiley and Hiroko Bretz:

The large cabin was in total darkness.
Come marching up the eastern hill afar.
When is the clock on the stairs dangerous?
Everything seemed so near and yet so far.
Behind the wall silence alone replied.
Was, then, even the staircase occupied?
Generating the rhymes is easy enough, the sentence structure a little trickier, but what’s impressive about this sonnet is that it sticks to a single topic and appears to be a more coherent whole. I’d guess they used associated “lexical fields” of similar words to help generate something coherent. In a similar way, most of the more famous examples of AI-generated music still involve some amount of human control, even if it’s editorial; a human will build a song around an AI-generated riff, or select the most convincing Bach chorale from amidst many different samples.

We are seeing strides forward in the ability of AI to generate human voices and human likenesses. As the latter example shows, in the fake news era people have focused on the dangers of this tech– but might it also be possible to create a virtual performer, trained on a dataset of their original music? Did you ever want to hear another Beatles album, or jam with Miles Davis? Of course, these things are impossible—but could we create a similar experience that people would genuinely value? Even, to the untrained eye, something indistinguishable from the real thing?

And if it did measure up to the real thing, what would this mean? Jaron Lanier is a fascinating technology writer, a critic of strong AI, and a believer in the power of virtual reality to change the world and provide truly meaningful experiences. He’s also a composer and a musical aficionado. He pointed out in a recent interview that translation algorithms, by reducing the amount of work translators are commissioned to do, have, in some sense, profited from stolen expertise. They were trained on huge datasets purloined from human linguists and translators. If you can train an AI on someone’s creative output and it produces new music, who “owns” it?

Although companies that offer AI music tools are starting to proliferate, and some groups will argue that the musical Turing test has been passed already, AI-generated music is hardly racing to the top of the pop charts just yet. Even as the line between human-composed and AI-generated music starts to blur, there’s still a gulf between the average human and musical genius. In the next few years, we’ll see how far the current techniques can take us. It may be the case that there’s something in the skylark’s song that can’t be generated by machines. But maybe not, and then this song might need an extra verse.

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#432456 This Planned Solar Farm in Saudi Arabia ...

Right now it only exists on paper, in the form of a memorandum of understanding. But if constructed, the newly-announced solar photovoltaic project in Saudi Arabia would break an astonishing array of records. It’s larger than any solar project currently planned by a factor of 100. When completed, nominally in 2030, it would have a capacity of an astonishing 200 gigawatts (GW). The project is backed by Softbank Group and Saudi Arabia’s new crown prince, Mohammed Bin Salman, and was announced in New York on March 27.

The Tengger Desert Solar Park in China, affectionately known as the “Great Wall of Solar,” is the world’s largest operating solar farm, with a capacity of 1.5 GW. Larger farms are under construction, including the Westlands Solar Park, which plans to finish with 2.7 GW of capacity. But even those that are only in the planning phases are dwarfed by the Saudi project; two early-stage solar parks will have capacity of 7.2 GW, and the plan involves them generating electricity as early as next year.

It makes more sense to compare to slightly larger projects, like nations, or even planets. Saudi Arabia’s current electricity generation capacity is 77 GW. This project would almost triple it. The current total solar photovoltaic generation capacity installed worldwide is 303 GW. In other words, this single solar farm would account for a similar installed capacity as the entire world’s capacity in 2015, and over a thousand times more than we had in 2000.

That’s exponential growth for you, folks.

Of course, practically doubling the world’s solar capacity doesn’t come cheap; the nominal estimate for the budget is around $200 billion (compared to $20 billion for around half a gigawatt of fusion, though, it may not seem so bad.) But the project would help solve a number of pressing problems for Saudi Arabia.

For a start, solar power works well in the desert. The irradiance is high, you have plenty of empty space, and peak demand is driven by air conditioning in the cities and so corresponds with peak supply. Even if oil companies might seem blasé about the global supply of oil running out, individual countries are aware that their own reserves won’t last forever, and they don’t want to miss the energy transition. The country’s Vision 2030 project aims to diversify its heavily oil-dependent economy by that year. If they can construct solar farms on this scale, alongside the $80 billion the government plans to spend on a fleet of nuclear reactors, it seems logical to export that power to other countries in the region, especially given the amount of energy storage that would be required otherwise.

We’ve already discussed a large-scale project to build solar panels in the desert then export the electricity: the DESERTEC initiative in the Sahara. Although DESERTEC planned a range of different demonstration plants on scales of around 500 MW, its ultimate ambition was to “provide 20 percent of Europe’s electricity by 2050.” It seems that this project is similar in scale to what they were planning. Weaning ourselves off fossil fuels is going to be incredibly difficult. Only large-scale nuclear, wind, or solar can really supply the world’s energy needs if consumption is anything like what it is today; in all likelihood, we’ll need a combination of all three.

To make a sizeable contribution to that effort, the renewable projects have to be truly epic in scale. The planned 2 GW solar park at Bulli Creek in Australia would cover 5 square kilometers, so it’s not unreasonable to suggest that, across many farms, this project could cover around 500 square kilometers—around the size of Chicago.

It will come as no surprise that Softbank is involved in this project. The founder, Masayoshi Son, is well-known for large-scale “visionary” investments. This is suggested by the name of his $100 billion VC fund, the Softbank Vision Fund, and the focus of its investments. It has invested millions of dollars in tech companies like Uber, IoT, NVIDIA and ARM, and startups across fields like VR, agritech, and AI.

Of course, Softbank is also the company that bought infamous robot-makers Boston Dynamics from Google when their not-at-all-sinister “Project Replicant” was sidelined. Softbank is famous in Japan in part due to their mascot, Pepper, which is probably the most widespread humanoid robot on the planet. Suffice it to say that Softbank is keen to be a part of any technological development, and they’re not afraid of projects that are truly vast in scope.

Since the Fukushima disaster in 2011 led Japan to turn away from nuclear power, Son has also been focused on green electricity, floating the idea of an Asia Super Grid. Similar to DESERTEC, it aims to get around the main issues with renewable energy (the land use and the intermittency of supply) with a vast super-grid that would connect Mongolia, India, Japan, China, Russia, and South Korea with high-voltage DC power cables. “Since this is such a grandiose project, many people told me it is crazy,” Son said. “They said it is impossible both economically and politically.” The first stage of the project, a demonstration wind farm of 50 megawatts in Mongolia, began operating in October of last year.

Given that Saudi Arabia put up $45 billion of the Vision Fund, it’s also not surprising to see the location of the project; Softbank reportedly had plans to invest $25 billion of the Vision Fund in Saudi Arabia, and $1 billion will be spent on the first solar farms there. Prince Mohammed Bin Salman, 32, who recently consolidated power, is looking to be seen on the global stage as a modernizer. He was effusive about the project. “It’s a huge step in human history,” he said. “It’s bold, risky, and we hope we succeed doing that.”

It is the risk that will keep renewable energy enthusiasts concerned.

Every visionary plan contains the potential for immense disappointment. As yet, the Asian Super Grid and the Saudi power plan are more or less at the conceptual stage. The fact that a memorandum of understanding exists between the Saudi government and Softbank is no guarantee that it will ever be built. Some analysts in the industry are a little skeptical.

“It’s an unprecedented construction effort; it’s an unprecedented financing effort,” said Benjamin Attia, a global solar analyst for Green Tech Media Research. “But there are so many questions, so few details, and a lot of headwinds, like grid instability, the availability of commercial debt, construction, and logistics challenges.”

We have already seen with the DESERTEC initiative that these vast-scale renewable energy projects can fail, despite immense enthusiasm. They are not easy to accomplish. But in a world without fossil fuels, they will be required. This project could be a flagship example for how to run a country on renewable energy—or another example of grand designs and good intentions. We’ll have to wait to find out which.

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#432431 Why Slowing Down Can Actually Help Us ...

Leah Weiss believes that when we pay attention to how we do our work—our thoughts and feelings about what we do and why we do it—we can tap into a much deeper reservoir of courage, creativity, meaning, and resilience.

As a researcher, educator, and author, Weiss teaches a course called “Leading with Compassion and Mindfulness” at the Stanford Graduate School of Business, one of the most competitive MBA programs in the world, and runs programs at HopeLab.

Weiss is the author of the new book How We Work: Live Your Purpose, Reclaim your Sanity and Embrace the Daily Grind, endorsed by the Dalai Lama, among others. I caught up with Leah to learn more about how the practice of mindfulness can deepen our individual and collective purpose and passion.

Lisa Kay Solomon: We’re hearing a lot about mindfulness these days. What is mindfulness and why is it so important to bring into our work? Can you share some of the basic tenets of the practice?

Leah Weiss, PhD: Mindfulness is, in its most literal sense, “the attention to inattention.” It’s as simple as noticing when you’re not paying attention and then re-focusing. It is prioritizing what is happening right now over internal and external noise.

The ability to work well with difficult coworkers, handle constructive feedback and criticism, regulate emotions at work—all of these things can come from regular mindfulness practice.

Some additional benefits of mindfulness are a greater sense of compassion (both self-compassion and compassion for others) and a way to seek and find purpose in even mundane things (and especially at work). From the business standpoint, mindfulness at work leads to increased productivity and creativity, mostly because when we are focused on one task at a time (as opposed to multitasking), we produce better results.

We spend more time with our co-workers than we do with our families; if our work relationships are negative, we suffer both mentally and physically. Even worse, we take all of those negative feelings home with us at the end of the work day. The antidote to this prescription for unhappiness is to have clear, strong purpose (one third of people do not have purpose at work and this is a major problem in the modern workplace!). We can use mental training to grow as people and as employees.

LKS: What are some recommendations you would make to busy leaders who are working around the clock to change the world?

LW: I think the most important thing is to remember to tend to our relationship with ourselves while trying to change the world. If we’re beating up on ourselves all the time we’ll be depleted.

People passionate about improving the world can get into habits of believing self-care isn’t important. We demand a lot of ourselves. It’s okay to fail, to mess up, to make mistakes—what’s important is how we learn from those mistakes and what we tell ourselves about those instances. What is the “internal script” playing in your own head? Is it positive, supporting, and understanding? It should be. If it isn’t, you can work on it. And the changes you make won’t just improve your quality of life, they’ll make you more resilient to weather life’s inevitable setbacks.

A close second recommendation is to always consider where everyone in an organization fits and help everyone (including yourself) find purpose. When you know what your own purpose is and show others their purpose, you can motivate a team and help everyone on a team gain pride in and at work. To get at this, make sure to ask people on your team what really lights them up. What sucks their energy and depletes them? If we know our own answers to these questions and relate them to the people we work with, we can create more engaged organizations.

LKS: Can you envision a future where technology and mindfulness can work together?

LW: Technology and mindfulness are already starting to work together. Some artificial intelligence companies are considering things like mindfulness and compassion when building robots, and there are numerous apps that target spreading mindfulness meditations in a widely-accessible way.

LKS: Looking ahead at our future generations who seem more attached to their devices than ever, what advice do you have for them?

LW: It’s unrealistic to say “stop using your device so much,” so instead, my suggestion is to make time for doing things like scrolling social media and make the same amount of time for putting your phone down and watching a movie or talking to a friend. No matter what it is that you are doing, make sure you have meta-awareness or clarity about what you’re paying attention to. Be clear about where your attention is and recognize that you can be a steward of attention. Technology can support us in this or pull us away from this; it depends on how we use it.

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