This article is part of a special issue of VB. Read the full series here: How data privacy is transforming marketing.
Many observers were disappointed with the recent AI-enabled demo”Optimusrobot on Tesla’s AI Day. One reviewer cleverly titled his article “Sub-Optimus.” However, these views actually miss the point. Whatever can be said of Elon Musk, he is a genius at sensing timing and opportunity, applying technology and providing the necessary resources.
The quality and enthusiasm of the engineering team suggests that Optimus could succeed, even if it takes longer than the estimate of 3 to 5 years for full production. If successful, Optimus could bring personal robots into the mainstream within a decade.
Though initially expensive at an estimated $20,000, an Optimus brother could be as commonplace in stores or factories by 2032 as Tesla is on the road today. Fast forward another 10 years and humanoid robots in everyday life could be commonplace, be it at home or in shops and restaurants, in factories and warehouses, or in health and home care environments.
AI hype: interacting with robots
In this vision, the idea of an “artificial friend,” an emotionally intelligent android as portrayed by Kazuo Ishiguro in Clara and the sun, doesn’t seem so far-fetched. Nor do “digients” (short for “digital entities”), as described by Ted Chiang in The lifecycle of software objects. Digients are artificial intelligences created in a purely digital world that inhabit a digital shared space (much like the emerging metaverse), but can also be downloaded into physical robots so they can interact with humans in the real world.
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This ability of humans to interact with a robot seems to be the key to a successful robot implementation. At least that’s the view of Will Jackson, the founder and CEO of Technical Artswho recently? said: “The ‘true killer app’ for a humanoid robot is people’s desire to interact with it.”
Is it possible that this robotic vision is totally unrealistic and little more than science fiction or entrepreneurial hype? That’s the view of some, says Michael Hiltzik of the Los Angeles Times. He stated: “AI hype is not only a danger to laymen’s understanding of the… [robotics] field, but there is a danger of it undermining the field itself.” In this he is right and it is certainly important to take the hype out of reality.
What Hiltzik may be missing is the arc of history. Robotics, like the expanding field of artificial intelligence (AI), is still in its infancy today. However, the pace of progress is phenomenal. While Optimus is still years away from a finished product and there are still plenty of technical and cultural hurdles, it’s impossible to ignore the extraordinary pace of progress. In just one year, Optimus went from concept to a mobile, bipedal robot. It’s a growing field, because Tesla isn’t alone in building a humanoid robot. For example, a team of engineers at the Rochester Institute of Technology (RIT) announced a humanoid robot that: Teaching Tai Chi.
Still a long way to go to achieve AI-powered robots
Building robots that mimic human actions is extremely difficult. A EE Times article describes these challenges. “For example, from a mechanical point of view, bipedal locomotion (walking on two legs) is an extremely physically demanding task. In response, the human body has evolved and adapted in such a way that the force density of human joints in areas such as the knees is very high.” In other words, simply staying upright is very difficult for robots.
Despite such challenges, real progress is being made. Oregon State University researchers recently set a Guinness World Record for a robot that 100 meters sprint, completing the course in less than 25 seconds. The team has been training “Cassie” since 2017, using AI reinforcement learning algorithms to reward the robot when it moves correctly. The importance of the record was noted by the lead researcher who: said: “[Now we] robots to move robustly around the world on two legs.” Though impressive, the human body not only stays upright, but navigates the world through an extremely intricate sensory system.
“The hardest thing is to make a machine that interacts with people naturally”, according to to Nancy J. Cooke, a professor at Arizona State University. That imitation in a robot is still in its infancy. That is now one of the biggest challenges facing Optimus and other humanoid robot efforts.
AI automation takes center stage
Humanoid robots are made possible by AI, and AI is hurtling forward, aided by the threefold exponential growth of computing power, software development and data.
Perhaps nowhere is this rapid AI progress better illustrated than in natural language processing (NLP), especially with regard to text and text-to-image generation. OpenAI released its first text generation tools in February 2019 with GPT-2, followed by GPT-3 in June 2020 and the text-to-image DALL-E in January 2021 and DALL-E 2 in April 2022. Each iteration was much better at than previous versions.
Other companies are promoting these technologies, such as MidJourney and Stable Diffusion. Now the same phenomenon is happening with text-to-video, with several new apps recently released from meta, google, Synthesis, GliaCloud and others.
NLP technologies are quickly finding applications in the real world, from code development to advertising (from copywriting to image creation) and even movie making. In my last article I described how creative artist Karen X. Cheng was tasked with creating an AI-generated cover photo in front of cosmopolitan. To help create ideas and the final image, she used DALL-E 2.
The crow, an AI-generated video, recently won the Jury Award at the Cannes Short Film Festival. Until make the videocomputer artist Glenn Marshall fed the video frames of an existing video as a reference to CLAMP (Contrastive Language-Image Pre-training), another text-to-image neural network also created by OpenAI. Marshall then asked CLIP to: generate a video from “a painting of a crow in a desolate landscape.”
If only it had a brain
Of course, building an NLP application is not the same as robotics. While computing power, software, and data are similar, the physical aspect of building robots to interact with the real world adds challenges beyond developing software automation. What robots need is a brain. AI researcher Filip Piekniewski told Business Insider that “robots have nothing even close to a brain.” That’s largely true today, but what NLP offers is the beginning of the brain that robots need to interact with humans. After all, an important humanoid brain function is the ability to perceive and interpret language and to convert that into contextually appropriate responses and actions.
NLP is already being used in chatbots, software robots that facilitate communication with people. December project, a text-based chatbot developed using GPT-3 – has helped people overcome disconnection by “talking” to a deceased loved one. Bot developer Jason Rohrer said from Project December: “It may not be the first intelligent machine. But it kind of feels like it’s the first machine with a soul.” Intelligent robots with a soul that can walk and manipulate objects would be a great advance.
That progress is near, although it could be another decade or more before humanoid robots roam the world. Optimus and other robots today are mostly simple machines that will grow in capacity over the next few decades to become fully evolved artificial humanoids. We have now really entered the modern era of robots.
Gary Grossman is the senior VP of technology practice at Nobleman and global leader of the Edelman AI Center of Excellence.
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