Tag Archives: technologies
Toyota Motor Corporation today revealed T-HR3, the company's third generation humanoid robot. Toyota's latest robotics platform, designed and developed by Toyota's Partner Robot Division, will explore new technologies for safely managing physical interactions between robots and their surroundings, as well as a new remote maneuvering system that mirrors user movements to the robot. Continue reading
Last week, Eric Schmidt, chairman of Alphabet, predicted that China will rapidly overtake the US in artificial intelligence…in as little as five years.
Last month, China announced plans to open a $10 billion quantum computing research center in 2020.
Bottom line, China is aggressively investing in exponential technologies, pursuing a bold goal of becoming the global AI superpower by 2030.
Based on what I’ve observed from China’s entrepreneurial scene, I believe they have a real shot of hitting that goal.
As I described in a previous tech blog, I recently traveled to China with a group of my Abundance 360 members, where I was hosted by my friend Kai-Fu Lee, the founder, chairman, and CEO of Sinovation Ventures.
On one of our first nights, Kai-Fu invited us to a special dinner at Da Dong Roast, which specializes in Peking duck, where we shared an 18-course meal.
The meal was amazing, and Kai-Fu’s dinner conversation provided us priceless insights on Chinese entrepreneurs.
Three topics opened my eyes. Here’s the wisdom I’d like to share with you.
1. The Entrepreneurial Culture in China
Chinese entrepreneurship has exploded onto the scene and changed significantly over the past 10 years.
In my opinion, one significant way that Chinese entrepreneurs vary from their American counterparts is in work ethic. The mantra I found in the startups I visited in Beijing and Shanghai was “9-9-6”—meaning the employees only needed to work from 9 am to 9 pm, 6 days a week.
Another concept Kai-Fu shared over dinner was the almost ‘dictatorial’ leadership of the founder/CEO. In China, it’s not uncommon for the Founder/CEO to own the majority of the company, or at least 30–40 percent. It’s also the case that what the CEO says is gospel. Period, no debate. There is no minority or dissenting opinion. When the CEO says “march,” the company asks, “which way?”
When Kai-Fu started Sinovation (his $1 billion+ venture fund), there were few active angel investors. Today, China has a rich ecosystem of angel, venture capital, and government-funded innovation parks.
As venture capital in China has evolved, so too has the mindset of the entrepreneur.
Kai -Fu recalled an early investment he made in which, after an unfortunate streak, the entrepreneur came to him, almost in tears, apologizing for losing his money and promising he would earn it back for him in another way. Kai-Fu comforted the entrepreneur and said there was no such need.
Only a few years later, the situation was vastly different. An entrepreneur who was going through a similar unfortunate streak came to Kai Fu and told him he only had $2 million left of his initial $12 million investment. He informed him he saw no value in returning the money and instead was going to take the last $2 million and use it as a final push to see if the company could succeed. He then promised Kai-Fu if he failed, he would remember what Kai-Fu did for him and, as such, possibly give Sinovation an opportunity to invest in him with his next company.
2. Chinese Companies Are No Longer Just ‘Copycats’
During dinner, Kai-Fu lamented that 10 years ago, it would be fair to call Chinese companies copycats of American companies. Five years ago, the claim would be controversial. Today, however, Kai-Fu is clear that claim is entirely false.
While smart Chinese startups will still look at what American companies are doing and build on trends, today it’s becoming a wise business practice for American tech giants to analyze Chinese companies. If you look at many new features of Facebook’s Messenger, it seems to very closely mirror TenCent’s WeChat.
Interestingly, tight government controls in China have actually spurred innovation. Take TV, for example, a highly regulated industry. Because of this regulation, most entertainment in China is consumed on the internet or by phone. Game shows, reality shows, and more will be entirely centered online.
Kai-Fu told us about one of his investments in a company that helps create Chinese singing sensations. They take girls in from a young age, school them, and regardless of talent, help build their presence and brand as singers. Once ready, these singers are pushed across all the available platforms, and superstars are born. The company recognizes its role in this superstar status, though, which is why it takes a 50 percent cut of all earnings.
This company is just one example of how Chinese entrepreneurs take advantage of China’s unique position, market, and culture.
3. China’s Artificial Intelligence Play
Kai-Fu wrapped up his talk with a brief introduction into the expansive AI industry in China. I previously discussed Face++, a Sinovation investment, which is creating radically efficient facial recognition technology. Face++ is light years ahead of anyone else globally at recognition in live videos. However, Face++ is just one of the incredible advances in AI coming out of China.
Baidu, one of China’s most valuable tech companies, started out as just a search company. However, they now run one of the country’s leading self-driving car programs.
Baidu’s goal is to create a software suite atop existing hardware that will control all self-driving aspects of a vehicle but also be able to provide additional services such as HD mapping and more.
Another interesting application came from another of Sinovation’s investments, Smart Finance Group (SFG). Given most payments are mobile (through WeChat or Alipay), only ~20 percent of the population in China have a credit history. This makes it very difficult for individuals in China to acquire a loan.
SFG’s mobile application takes in user data (as much as the user allows) and, based on the information provided, uses an AI agent to create a financial profile with the power to offer an instant loan. This loan can be deposited directly into their WeChat or Alipay account and is typically approved in minutes. Unlike American loan companies, they avoid default and long-term debt by only providing a one-month loan with 10% interest. Borrow $200, and you pay back $220 by the following month.
Artificial intelligence is exploding in China, and Kai-Fu believes it will touch every single industry.
The only constant is change, and the rate of change is constantly increasing.
In the next 10 years, we’ll see tremendous changes on the geopolitical front and the global entrepreneurial scene caused by technological empowerment.
China is an entrepreneurial hotbed that cannot be ignored. I’m monitoring it closely. Are you?
Image Credit: anekoho / Shutterstock.com Continue reading
Major websites all over the world use a system called CAPTCHA to verify that someone is indeed a human and not a bot when entering data or signing into an account. CAPTCHA stands for the “Completely Automated Public Turing test to tell Computers and Humans Apart.” The squiggly letters and numbers, often posted against photographs or textured backgrounds, have been a good way to foil hackers. They are annoying but effective.
The days of CAPTCHA as a viable line of defense may, however, be numbered.
Researchers at Vicarious, a Californian artificial intelligence firm funded by Amazon founder Jeff Bezos and Facebook’s Mark Zuckerberg, have just published a paper documenting how they were able to defeat CAPTCHA using new artificial intelligence techniques. Whereas today’s most advanced artificial intelligence (AI) technologies use neural networks that require massive amounts of data to learn from, sometimes millions of examples, the researchers said their system needed just five training steps to crack Google’s reCAPTCHA technology. With this, they achieved a 67 percent success rate per character—reasonably close to the human accuracy rate of 87 percent. In answering PayPal and Yahoo CAPTCHAs, the system achieved an accuracy rate of greater than 50 percent.
The CAPTCHA breakthrough came hard on the heels of another major milestone from Google’s DeepMind team, the people who built the world’s best Go-playing system. DeepMind built a new artificial-intelligence system called AlphaGo Zero that taught itself to play the game at a world-beating level with minimal training data, mainly using trial and error—in a fashion similar to how humans learn.
Both playing Go and deciphering CAPTCHAs are clear examples of what we call narrow AI, which is different from artificial general intelligence (AGI)—the stuff of science fiction. Remember R2-D2 of Star Wars, Ava from Ex Machina, and Samantha from Her? They could do many things and learned everything they needed on their own.
Narrow AI technologies are systems that can only perform one specific type of task. For example, if you asked AlphaGo Zero to learn to play Monopoly, it could not, even though that is a far less sophisticated game than Go. If you asked the CAPTCHA cracker to learn to understand a spoken phrase, it would not even know where to start.
To date, though, even narrow AI has been difficult to build and perfect. To perform very elementary tasks such as determining whether an image is of a cat or a dog, the system requires the development of a model that details exactly what is being analyzed and massive amounts of data with labeled examples of both. The examples are used to train the AI systems, which are modeled on the neural networks in the brain, in which the connections between layers of neurons are adjusted based on what is observed. To put it simply, you tell an AI system exactly what to learn, and the more data you give it, the more accurate it becomes.
The methods that Vicarious and Google used were different; they allowed the systems to learn on their own, albeit in a narrow field. By making their own assumptions about what the training model should be and trying different permutations until they got the right results, they were able to teach themselves how to read the letters in a CAPTCHA or to play a game.
This blurs the line between narrow AI and AGI and has broader implications in robotics and virtually any other field in which machine learning in complex environments may be relevant.
Beyond visual recognition, the Vicarious breakthrough and AlphaGo Zero success are encouraging scientists to think about how AIs can learn to do things from scratch. And this brings us one step closer to coexisting with classes of AIs and robots that can learn to perform new tasks that are slight variants on their previous tasks—and ultimately the AGI of science fiction.
So R2-D2 may be here sooner than we expected.
This article was originally published by The Washington Post. Read the original article here.
Image Credit: Zapp2Photo / Shutterstock.com Continue reading
Many people get frustrated with technology when it malfunctions or is counterintuitive. The last thing people might expect is for that same technology to pick up on their emotions and engage with them differently as a result.
All of that is now changing. Computers are increasingly able to figure out what we’re feeling—and it’s big business.
A recent report predicts that the global affective computing market will grow from $12.2 billion in 2016 to $53.98 billion by 2021. The report by research and consultancy firm MarketsandMarkets observed that enabling technologies have already been adopted in a wide range of industries and noted a rising demand for facial feature extraction software.
Affective computing is also referred to as emotion AI or artificial emotional intelligence. Although many people are still unfamiliar with the category, researchers in academia have already discovered a multitude of uses for it.
At the University of Tokyo, Professor Toshihiko Yamasaki decided to develop a machine learning system that evaluates the quality of TED Talk videos. Of course, a TED Talk is only considered to be good if it resonates with a human audience. On the surface, this would seem too qualitatively abstract for computer analysis. But Yamasaki wanted his system to watch videos of presentations and predict user impressions. Could a machine learning system accurately evaluate the emotional persuasiveness of a speaker?
Yamasaki and his colleagues came up with a method that analyzed correlations and “multimodal features including linguistic as well as acoustic features” in a dataset of 1,646 TED Talk videos. The experiment was successful. The method obtained “a statistically significant macro-average accuracy of 93.3 percent, outperforming several competitive baseline methods.”
A machine was able to predict whether or not a person would emotionally connect with other people. In their report, the authors noted that these findings could be used for recommendation purposes and also as feedback to the presenters, in order to improve the quality of their public presentation. However, the usefulness of affective computing goes far beyond the way people present content. It may also transform the way they learn it.
Researchers from North Carolina State University explored the connection between students’ affective states and their ability to learn. Their software was able to accurately predict the effectiveness of online tutoring sessions by analyzing the facial expressions of participating students. The software tracked fine-grained facial movements such as eyebrow raising, eyelid tightening, and mouth dimpling to determine engagement, frustration, and learning. The authors concluded that “analysis of facial expressions has great potential for educational data mining.”
This type of technology is increasingly being used within the private sector. Affectiva is a Boston-based company that makes emotion recognition software. When asked to comment on this emerging technology, Gabi Zijderveld, chief marketing officer at Affectiva, explained in an interview for this article, “Our software measures facial expressions of emotion. So basically all you need is our software running and then access to a camera so you can basically record a face and analyze it. We can do that in real time or we can do this by looking at a video and then analyzing data and sending it back to folks.”
The technology has particular relevance for the advertising industry.
Zijderveld said, “We have products that allow you to measure how consumers or viewers respond to digital content…you could have a number of people looking at an ad, you measure their emotional response so you aggregate the data and it gives you insight into how well your content is performing. And then you can adapt and adjust accordingly.”
Zijderveld explained that this is the first market where the company got traction. However, they have since packaged up their core technology in software development kits or SDKs. This allows other companies to integrate emotion detection into whatever they are building.
By licensing its technology to others, Affectiva is now rapidly expanding into a wide variety of markets, including gaming, education, robotics, and healthcare. The core technology is also used in human resources for the purposes of video recruitment. The software analyzes the emotional responses of interviewees, and that data is factored into hiring decisions.
Richard Yonck is founder and president of Intelligent Future Consulting and the author of a book about our relationship with technology. “One area I discuss in Heart of the Machine is the idea of an emotional economy that will arise as an ecosystem of emotionally aware businesses, systems, and services are developed. This will rapidly expand into a multi-billion-dollar industry, leading to an infrastructure that will be both emotionally responsive and potentially exploitive at personal, commercial, and political levels,” said Yonck, in an interview for this article.
According to Yonck, these emotionally-aware systems will “better anticipate needs, improve efficiency, and reduce stress and misunderstandings.”
Affectiva is uniquely positioned to profit from this “emotional economy.” The company has already created the world’s largest emotion database. “We’ve analyzed a little bit over 4.7 million faces in 75 countries,” said Zijderveld. “This is data first and foremost, it’s data gathered with consent. So everyone has opted in to have their faces analyzed.”
The vastness of that database is essential for deep learning approaches. The software would be inaccurate if the data was inadequate. According to Zijderveld, “If you don’t have massive amounts of data of people of all ages, genders, and ethnicities, then your algorithms are going to be pretty biased.”
This massive database has already revealed cultural insights into how people express emotion. Zijderveld explained, “Obviously everyone knows that women are more expressive than men. But our data confirms that, but not only that, it can also show that women smile longer. They tend to smile more often. There’s also regional differences.”
Yonck believes that affective computing will inspire unimaginable forms of innovation and that change will happen at a fast pace.
He explained, “As businesses, software, systems, and services develop, they’ll support and make possible all sorts of other emotionally aware technologies that couldn’t previously exist. This leads to a spiral of increasingly sophisticated products, just as happened in the early days of computing.”
Those who are curious about affective technology will soon be able to interact with it.
Hubble Connected unveiled the Hubble Hugo at multiple trade shows this year. Hugo is billed as “the world’s first smart camera,” with emotion AI video analytics powered by Affectiva. The product can identify individuals, figure out how they’re feeling, receive voice commands, video monitor your home, and act as a photographer and videographer of events. Media can then be transmitted to the cloud. The company’s website describes Hugo as “a fun pal to have in the house.”
Although he sees the potential for improved efficiencies and expanding markets, Richard Yonck cautions that AI technology is not without its pitfalls.
“It’s critical that we understand we are headed into very unknown territory as we develop these systems, creating problems unlike any we’ve faced before,” said Yonck. “We should put our focus on ensuring AI develops in a way that represents our human values and ideals.”
Image Credit: Kisan / Shutterstock.com Continue reading