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#431315 Better Than Smart Speakers? Japan Is ...

While American internet giants are developing speakers, Japanese companies are working on robots and holograms. They all share a common goal: to create the future platform for the Internet of Things (IoT) and smart homes.
Names like Bocco, EMIEW3, Xperia Assistant, and Gatebox may not ring a bell to most outside of Japan, but Sony, Hitachi, Sharp, and Softbank most certainly do. The companies, along with Japanese start-ups, have developed robots, robot concepts, and even holograms like the ones hiding behind the short list of names.
While there are distinct differences between the various systems, they share the potential to act as a remote control for IoT devices and smart homes. It is a very different direction than that taken by companies like Google, Amazon, and Apple, who have so far focused on building IoT speaker systems.
Bocco robot. Image Credit: Yukai Engineering
“Technology companies are pursuing the platform—or smartphone if you will—for IoT. My impression is that Japanese companies—and Japanese consumers—prefer that such a platform should not just be an object, but a companion,” says Kosuke Tatsumi, designer at Yukai Engineering, a startup that has developed the Bocco robot system.
At Hitachi, a spokesperson said that the company’s human symbiotic service robot, EMIEW3, robot is currently in the field, doing proof-of-value tests at customer sites to investigate needs and potential solutions. This could include working as an interactive control system for the Internet of Things:
“EMIEW3 is able to communicate with humans, thus receive instructions, and as it is connected to a robotics IT platform, it is very much capable of interacting with IoT-based systems,” the spokesperson said.
The power of speech is getting feet
Gartner analysis predicts that there will be 8.4 billion internet-connected devices—collectively making up the Internet of Things—by the end of 2017. 5.2 billion of those devices are in the consumer category. By the end of 2020, the number of IoT devices will rise to 12.8 billion—and that is just in the consumer category.
As a child of the 80s, I can vividly remember how fun it was to have separate remote controls for TV, video, and stereo. I can imagine a situation where my internet-connected refrigerator and ditto thermostat, television, and toaster try to work out who I’m talking to and what I want them to do.
Consensus seems to be that speech will be the way to interact with many/most IoT devices. The same goes for a form of virtual assistant functioning as the IoT platform—or remote control. Almost everything else is still an open ballgame, despite an early surge for speaker-based systems, like those from Amazon, Google, and Apple.
Why robots could rule
Famous android creator and robot scientist Dr. Hiroshi Ishiguro sees the interaction between humans and the AI embedded in speakers or robots as central to both approaches. From there, the approaches differ greatly.
Image Credit: Hiroshi Ishiguro Laboratories
“It is about more than the difference of form. Speaking to an Amazon Echo is not a natural kind of interaction for humans. That is part of what we in Japan are creating in many human-like robot systems,” he says. “The human brain is constructed to recognize and interact with humans. This is part of why it makes sense to focus on developing the body for the AI mind as well as the AI mind itself. In a way, you can describe it as the difference between developing an assistant, which could be said to be what many American companies are currently doing, and a companion, which is more the focus here in Japan.”
Another advantage is that robots are more kawaii—a multifaceted Japanese word that can be translated as “cute”—than speakers are. This makes it easy for people to relate to them and forgive them.
“People are more willing to forgive children when they make mistakes, and the same is true with a robot like Bocco, which is designed to look kawaii and childlike,” Kosuke Tatsumi explains.
Japanese robots and holograms with IoT-control capabilities
So, what exactly do these robot and hologram companions look like, what can they do, and who’s making them? Here are seven examples of Japanese companies working to go a step beyond smart speakers with personable robots and holograms.
1. In 2016 Sony’s mobile division demonstrated the Xperia Agent concept robot that recognizes individual users, is voice controlled, and can do things like control your television and receive calls from services like Skype.

2. Sharp launched their Home Assistant at CES 2016. A robot-like, voice-controlled assistant that can to control, among other things, air conditioning units, and televisions. Sharp has also launched a robotic phone called RoBoHon.
3. Gatebox has created a holographic virtual assistant. Evil tongues will say that it is primarily the expression of an otaku (Japanese for nerd) dream of living with a manga heroine. Gatebox is, however, able to control things like lights, TVs, and other systems through API integration. It also provides its owner with weather-related advice like “remember your umbrella, it looks like it will rain later.” Gatebox can be controlled by voice, gesture, or via an app.
4. Hitachi’s EMIEW3 robot is designed to assist people in businesses and public spaces. It is connected to a robot IT-platform via the cloud that acts as a “remote brain.” Hitachi is currently investigating the business use cases for EMIEW3. This could include the role of controlling platform for IoT devices.

5. Softbank’s Pepper robot has been used as a platform to control use of medical IoT devices such as smart thermometers by Avatarion. The company has also developed various in-house systems that enable Pepper to control IoT-devices like a coffee machine. A user simply asks Pepper to brew a cup of coffee, and it starts the coffee machine for you.
6. Yukai Engineering’s Bocco registers when a person (e.g., young child) comes home and acts as a communication center between that person and other members of the household (e.g., parent still at work). The company is working on integrating voice recognition, voice control, and having Bocco control things like the lights and other connected IoT devices.
7. Last year Toyota launched the Kirobo Mini, a companion robot which aims to, among other things, help its owner by suggesting “places to visit, routes for travel, and music to listen to” during the drive.

Today, Japan. Tomorrow…?
One of the key questions is whether this emerging phenomenon is a purely Japanese thing. If the country’s love of robots makes it fundamentally different. Japan is, after all, a country where new units of Softbank’s Pepper robot routinely sell out in minutes and the RoBoHon robot-phone has its own cafe nights in Tokyo.
It is a country where TV introduces you to friendly, helpful robots like Doraemon and Astro Boy. I, on the other hand, first met robots in the shape of Arnold Schwarzenegger’s Terminator and struggled to work out why robots seemed intent on permanently borrowing things like clothes and motorcycles, not to mention why they hated people called Sarah.
However, research suggests that a big part of the reason why Japanese seem to like robots is a combination of exposure and positive experiences that leads to greater acceptance of them. As robots spread to more and more industries—and into our homes—our acceptance of them will grow.
The argument is also backed by a project by Avatarion, which used Softbank’s Nao-robot as a classroom representative for children who were in the hospital.
“What we found was that the other children quickly adapted to interacting with the robot and treating it as the physical representation of the child who was in hospital. They accepted it very quickly,” Thierry Perronnet, General Manager of Avatarion, explains.
His company has also developed solutions where Softbank’s Pepper robot is used as an in-home nurse and controls various medical IoT devices.
If robots end up becoming our preferred method for controlling IoT devices, it is by no means certain that said robots will be coming from Japan.
“I think that the goal for both Japanese and American companies—including the likes of Google, Amazon, Microsoft, and Apple—is to create human-like interaction. For this to happen, technology needs to evolve and adapt to us and how we are used to interacting with others, in other words, have a more human form. Humans’ speed of evolution cannot keep up with technology’s, so it must be the technology that changes,” Dr. Ishiguro says.
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#431189 Researchers Develop New Tech to Predict ...

It is one of the top 10 deadliest diseases in the United States, and it cannot be cured or prevented. But new studies are finding ways to diagnose Alzheimer’s disease in its earliest stages, while some of the latest research says technologies like artificial intelligence can detect dementia years before the first symptoms occur.
These advances, in turn, will help bolster clinical trials seeking a cure or therapies to slow or prevent the disease. Catching Alzheimer’s disease or other forms of dementia early in their progression can help ease symptoms in some cases.
“Often neurodegeneration is diagnosed late when massive brain damage has already occurred,” says professor Francis L Martin at the University of Central Lancashire in the UK, in an email to Singularity Hub. “As we know more about the molecular basis of the disease, there is the possibility of clinical interventions that might slow or halt the progress of the disease, i.e., before brain damage. Extending cognitive ability for even a number of years would have huge benefit.”
Blood Diamond
Martin is the principal investigator on a project that has developed a technique to analyze blood samples to diagnose Alzheimer’s disease and distinguish between other forms of dementia.
The researchers used sensor-based technology with a diamond core to analyze about 550 blood samples. They identified specific chemical bonds within the blood after passing light through the diamond core and recording its interaction with the sample. The results were then compared against blood samples from cases of Alzheimer’s disease and other neurodegenerative diseases, along with those from healthy individuals.
“From a small drop of blood, we derive a fingerprint spectrum. That fingerprint spectrum contains numerical data, which can be inputted into a computational algorithm we have developed,” Martin explains. “This algorithm is validated for prediction of unknown samples. From this we determine sensitivity and specificity. Although not perfect, my clinical colleagues reliably tell me our results are far better than anything else they have seen.”
Martin says the breakthrough is the result of more than 10 years developing sensor-based technologies for routine screening, monitoring, or diagnosing neurodegenerative diseases and cancers.
“My vision was to develop something low-cost that could be readily applied in a typical clinical setting to handle thousands of samples potentially per day or per week,” he says, adding that the technology also has applications in environmental science and food security.
The new test can also distinguish accurately between Alzheimer’s disease and other forms of neurodegeneration, such as Lewy body dementia, which is one of the most common causes of dementia after Alzheimer’s.
“To this point, other than at post-mortem, there has been no single approach towards classifying these pathologies,” Martin notes. “MRI scanning is often used but is labor-intensive, costly, difficult to apply to dementia patients, and not a routine point-of-care test.”
Crystal Ball
Canadian researchers at McGill University believe they can predict Alzheimer’s disease up to two years before its onset using big data and artificial intelligence. They developed an algorithm capable of recognizing the signatures of dementia using a single amyloid PET scan of the brain of patients at risk of developing the disease.
Alzheimer’s is caused by the accumulation of two proteins—amyloid beta and tau. The latest research suggests that amyloid beta leads to the buildup of tau, which is responsible for damaging nerve cells and connections between cells called synapses.
The work was recently published in the journal Neurobiology of Aging.
“Despite the availability of biomarkers capable of identifying the proteins causative of Alzheimer’s disease in living individuals, the current technologies cannot predict whether carriers of AD pathology in the brain will progress to dementia,” Sulantha Mathotaarachchi, lead author on the paper and an expert in artificial neural networks, tells Singularity Hub by email.
The algorithm, trained on a population with amnestic mild cognitive impairment observed over 24 months, proved accurate 84.5 percent of the time. Mathotaarachchi says the algorithm can be trained on different populations for different observational periods, meaning the system can grow more comprehensive with more data.
“The more biomarkers we incorporate, the more accurate the prediction could be,” Mathotaarachchi adds. “However, right now, acquiring [the] required amount of training data is the biggest challenge. … In Alzheimer’s disease, it is known that the amyloid protein deposition occurs decades before symptoms onset.”
Unfortunately, the same process occurs in normal aging as well. “The challenge is to identify the abnormal patterns of deposition that lead to the disease later on,” he says
One of the key goals of the project is to improve the research in Alzheimer’s disease by ensuring those patients with the highest probability to develop dementia are enrolled in clinical trials. That will increase the efficiency of clinical programs, according to Mathotaarachchi.
“One of the most important outcomes from our study was the pilot, online, real-time prediction tool,” he says. “This can be used as a framework for patient screening before recruiting for clinical trials. … If a disease-modifying therapy becomes available for patients, a predictive tool might have clinical applications as well, by providing to the physician information regarding clinical progression.”
Pixel by Pixel Prediction
Private industry is also working toward improving science’s predictive powers when it comes to detecting dementia early. One startup called Darmiyan out of San Francisco claims its proprietary software can pick up signals before the onset of Alzheimer’s disease by up to 15 years.
Darmiyan didn’t respond to a request for comment for this article. Venture Beat reported that the company’s MRI-analyzing software “detects cell abnormalities at a microscopic level to reveal what a standard MRI scan cannot” and that the “software measures and highlights subtle microscopic changes in the brain tissue represented in every pixel of the MRI image long before any symptoms arise.”
Darmiyan claims to have a 90 percent accuracy rate and says its software has been vetted by top academic institutions like New York University, Rockefeller University, and Stanford, according to Venture Beat. The startup is awaiting FDA approval to proceed further but is reportedly working with pharmaceutical companies like Amgen, Johnson & Johnson, and Pfizer on pilot programs.
“Our technology enables smarter drug selection in preclinical animal studies, better patient selection for clinical trials, and much better drug-effect monitoring,” Darmiyan cofounder and CEO Padideh Kamali-Zare told Venture Beat.
Conclusions
An estimated 5.5 million Americans have Alzheimer’s, and one in 10 people over age 65 have been diagnosed with the disease. By mid-century, the number of Alzheimer’s patients could rise to 16 million. Health care costs in 2017 alone are estimated to be $259 billion, and by 2050 the annual price tag could be more than $1 trillion.
In sum, it’s a disease that cripples people and the economy.
Researchers are always after more data as they look to improve outcomes, with the hope of one day developing a cure or preventing the onset of neurodegeneration altogether. If interested in seeing this medical research progress, you can help by signing up on the Brain Health Registry to improve the quality of clinical trials.
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#431186 The Coming Creativity Explosion Belongs ...

Does creativity make human intelligence special?
It may appear so at first glance. Though machines can calculate, analyze, and even perceive, creativity may seem far out of reach. Perhaps this is because we find it mysterious, even in ourselves. How can the output of a machine be anything more than that which is determined by its programmers?
Increasingly, however, artificial intelligence is moving into creativity’s hallowed domain, from art to industry. And though much is already possible, the future is sure to bring ever more creative machines.
What Is Machine Creativity?
Robotic art is just one example of machine creativity, a rapidly growing sub-field that sits somewhere between the study of artificial intelligence and human psychology.
The winning paintings from the 2017 Robot Art Competition are strikingly reminiscent of those showcased each spring at university exhibitions for graduating art students. Like the works produced by skilled artists, the compositions dreamed up by the competition’s robotic painters are aesthetically ambitious. One robot-made painting features a man’s bearded face gazing intently out from the canvas, his eyes locking with the viewer’s. Another abstract painting, “inspired” by data from EEG signals, visually depicts the human emotion of misery with jagged, gloomy stripes of black and purple.
More broadly, a creative machine is software (sometimes encased in a robotic body) that synthesizes inputs to generate new and valuable ideas, solutions to complex scientific problems, or original works of art. In a process similar to that followed by a human artist or scientist, a creative machine begins its work by framing a problem. Next, its software specifies the requirements the solution should have before generating “answers” in the form of original designs, patterns, or some other form of output.
Although the notion of machine creativity sounds a bit like science fiction, the basic concept is one that has been slowly developing for decades.
Nearly 50 years ago while a high school student, inventor and futurist Ray Kurzweil created software that could analyze the patterns in musical compositions and then compose new melodies in a similar style. Aaron, one of the world’s most famous painting robots, has been hard at work since the 1970s.
Industrial designers have used an automated, algorithm-driven process for decades to design computer chips (or machine parts) whose layout (or form) is optimized for a particular function or environment. Emily Howell, a computer program created by David Cope, writes original works in the style of classical composers, some of which have been performed by human orchestras to live audiences.
What’s different about today’s new and emerging generation of robotic artists, scientists, composers, authors, and product designers is their ubiquity and power.

“The recent explosion of artificial creativity has been enabled by the rapid maturation of the same exponential technologies that have already re-drawn our daily lives.”

I’ve already mentioned the rapidly advancing fields of robotic art and music. In the realm of scientific research, so-called “robotic scientists” such as Eureqa and Adam and Eve develop new scientific hypotheses; their “insights” have contributed to breakthroughs that are cited by hundreds of academic research papers. In the medical industry, creative machines are hard at work creating chemical compounds for new pharmaceuticals. After it read over seven million words of 20th century English poetry, a neural network developed by researcher Jack Hopkins learned to write passable poetry in a number of different styles and meters.
The recent explosion of artificial creativity has been enabled by the rapid maturation of the same exponential technologies that have already re-drawn our daily lives, including faster processors, ubiquitous sensors and wireless networks, and better algorithms.
As they continue to improve, creative machines—like humans—will perform a broad range of creative activities, ranging from everyday problem solving (sometimes known as “Little C” creativity) to producing once-in-a-century masterpieces (“Big C” creativity). A creative machine’s outputs could range from a design for a cast for a marble sculpture to a schematic blueprint for a clever new gadget for opening bottles of wine.
In the coming decades, by automating the process of solving complex problems, creative machines will again transform our world. Creative machines will serve as a versatile source of on-demand talent.
In the battle to recruit a workforce that can solve complex problems, creative machines will put small businesses on equal footing with large corporations. Art and music lovers will enjoy fresh creative works that re-interpret the style of ancient disciplines. People with a health condition will benefit from individualized medical treatments, and low-income people will receive top-notch legal advice, to name but a few potentially beneficial applications.
How Can We Make Creative Machines, Unless We Understand Our Own Creativity?
One of the most intriguing—yet unsettling—aspects of watching robotic arms skillfully oil paint is that we humans still do not understand our own creative process. Over the centuries, several different civilizations have devised a variety of models to explain creativity.
The ancient Greeks believed that poets drew inspiration from a transcendent realm parallel to the material world where ideas could take root and flourish. In the Middle Ages, philosophers and poets attributed our peculiarly human ability to “make something of nothing” to an external source, namely divine inspiration. Modern academic study of human creativity has generated vast reams of scholarship, but despite the value of these insights, the human imagination remains a great mystery, second only to that of consciousness.
Today, the rise of machine creativity demonstrates (once again), that we do not have to fully understand a biological process in order to emulate it with advanced technology.
Past experience has shown that jet planes can fly higher and faster than birds by using the forward thrust of an engine rather than wings. Submarines propel themselves forward underwater without fins or a tail. Deep learning neural networks identify objects in randomly-selected photographs with super-human accuracy. Similarly, using a fairly straightforward software architecture, creative software (sometimes paired with a robotic body) can paint, write, hypothesize, or design with impressive originality, skill, and boldness.
At the heart of machine creativity is simple iteration. No matter what sort of output they produce, creative machines fall into one of three categories depending on their internal architecture.
Briefly, the first group consists of software programs that use traditional rule-based, or symbolic AI, the second group uses evolutionary algorithms, and the third group uses a variation of a form of machine learning called deep learning that has already revolutionized voice and facial recognition software.
1) Symbolic creative machines are the oldest artificial artists and musicians. In this approach—also known as “good old-fashioned AI (GOFAI) or symbolic AI—the human programmer plays a key role by writing a set of step-by-step instructions to guide the computer through a task. Despite the fact that symbolic AI is limited in its ability to adapt to environmental changes, it’s still possible for a robotic artist programmed this way to create an impressively wide variety of different outputs.
2) Evolutionary algorithms (EA) have been in use for several decades and remain powerful tools for design. In this approach, potential solutions “compete” in a software simulator in a Darwinian process reminiscent of biological evolution. The human programmer specifies a “fitness criterion” that will be used to score and rank the solutions generated by the software. The software then generates a “first generation” population of random solutions (which typically are pretty poor in quality), scores this first generation of solutions, and selects the top 50% (those random solutions deemed to be the best “fit”). The software then takes another pass and recombines the “winning” solutions to create the next generation and repeats this process for thousands (and sometimes millions) of generations.
3) Generative deep learning (DL) neural networks represent the newest software architecture of the three, since DL is data-dependent and resource-intensive. First, a human programmer “trains” a DL neural network to recognize a particular feature in a dataset, for example, an image of a dog in a stream of digital images. Next, the standard “feed forward” process is reversed and the DL neural network begins to generate the feature, for example, eventually producing new and sometimes original images of (or poetry about) dogs. Generative DL networks have tremendous and unexplored creative potential and are able to produce a broad range of original outputs, from paintings to music to poetry.
The Coming Explosion of Machine Creativity
In the near future as Moore’s Law continues its work, we will see sophisticated combinations of these three basic architectures. Since the 1950s, artificial intelligence has steadily mastered one human ability after another, and in the process of doing so, has reduced the cost of calculation, analysis, and most recently, perception. When creative software becomes as inexpensive and ubiquitous as analytical software is today, humans will no longer be the only intelligent beings capable of creative work.
This is why I have to bite my tongue when I hear the well-intended (but shortsighted) advice frequently dispensed to young people that they should pursue work that demands creativity to help them “AI-proof” their futures.
Instead, students should gain skills to harness the power of creative machines.
There are two skills in which humans excel that will enable us to remain useful in a world of ever-advancing artificial intelligence. One, the ability to frame and define a complex problem so that it can be handed off to a creative machine to solve. And two, the ability to communicate the value of both the framework and the proposed solution to the other humans involved.
What will happen to people when creative machines begin to capably tread on intellectual ground that was once considered the sole domain of the human mind, and before that, the product of divine inspiration? While machines engaging in Big C creativity—e.g., oil painting and composing new symphonies—tend to garner controversy and make the headlines, I suspect the real world-changing application of machine creativity will be in the realm of everyday problem solving, or Little C. The mainstream emergence of powerful problem-solving tools will help people create abundance where there was once scarcity.
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#431171 SceneScan: Real-Time 3D Depth Sensing ...

Nerian Introduces a High-Performance Successor for the Proven SP1 System
Stereo vision, which is the three-dimensional perception of our environment with two sensors likeour eyes, is a well-known technology. As a passive method – there is no need to emit light in thevisible or invisible spectral range – this technology can open up new possibilities for three dimensional perception, even under difficult conditions.
But as often, the devil is in the details: for most applications, the software implementation withstandard PCs, but also with graphics processors, is too slow. Another complicating factor is thatthese hardware platforms are expensive and not energy-efficient. The solution is to instead usespecialized hardware for image processing. A programmable logic device – a so-called FPGA – cangreatly accelerate the image processing.
As a technology leader, Nerian Vision Technologies has been following this path successfully forthe past two years with the SP1 stereo vision system, which has enabled completely newapplications in the fields of robotics, automation technology, medical technology, autonomousdriving and other domains. Now the company introduces two successors:
SceneScan and SceneScan Pro. Real eye-catchers in a double sense: stereo vision in an elegant design!But more important is, of course, the significantly improved inner workings of the two new modelsin comparison to their predecessor. The new hardware allows processing rates of up to 100 framesper second at resolutions of up to 3 megapixels, which leaves the SP1 far behind:
Photo Credit: Nerian Vision Technologies – www.nerian.com

The table illustrates the difference: while SceneScan Pro has the highest possible computing powerand is designed for the most demanding applications, SceneScan has been cost-reduced forapplications with lower requirements. The customer can thus optimize his embedded vision solution both in terms of costs and technology.
The new duo is completed by Nerian’s proven Karmin stereo cameras. Of course, industrialUSB3Vision cameras by other manufacturers are also supported.This combination not only supports the above-mentioned applications even better, but alsofacilitates completely new and innovative ones. If required, customer-specific adaptations are alsopossible.
ContactNerian Vision TechnologiesOwner: Dr. Konstantin SchauweckerGotenstr. 970771 Leinfelden-EchterdingenGermanyPhone: +49 711 / 2195 9414Email: service@nerian.comWebsite: http://nerian.com
Press Release Authored By: Nerian Vision Technologies
Photo Credit: Nerian Vision Technologies – www.nerian.com
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#431022 Robots and AI Will Take Over These 3 ...

We’re no stranger to robotics in the medical field. Robot-assisted surgery is becoming more and more common. Many training programs are starting to include robotic and virtual reality scenarios to provide hands-on training for students without putting patients at risk.
With all of these advances in medical robotics, three niches stand out above the rest: surgery, medical imaging, and drug discovery. How have robotics already begun to exert their influence on these practices, and how will they change them for good?
Robot-Assisted Surgery
Robot-assisted surgery was first documented in 1985, when it was used for a neurosurgical biopsy. This led to the use of robotics in a number of similar surgeries, both laparoscopic and traditional operations. The FDA didn’t approve robotic surgery tools until 2000, when the da Vinci Surgery system hit the market.
The robot-assisted surgery market is expected to grow steadily into 2023 and potentially beyond. The only thing that might stand in the way of this growth is the cost of the equipment. The initial investment may prevent small practices from purchasing the necessary devices.
Medical Imaging
The key to successful medical imaging isn’t the equipment itself. It’s being able to interpret the information in the images. Medical images are some of the most information-dense pieces of data in the medical field and can reveal so much more than a basic visual inspection can.
Robotics and, more specifically, artificial intelligence programs like IBM Watson can help interpret these images more efficiently and accurately. By allowing an AI or basic machine learning program to study the medical images, researchers can find patterns and make more accurate diagnoses than ever before.
Drug Discovery
Drug discovery is a long and often tedious process that includes years of testing and assessment. Artificial intelligence, machine learning and predictive algorithms could help speed up this system.
Imagine if researchers could input the kind of medicine they’re trying to make and the kind of symptoms they’re trying to treat into a computer and let it do the rest. With robotics, that may someday be possible.

This isn’t a perfect solution yet—these systems require massive amounts of data before they can start making decisions or predictions. By feeding data into the cloud where these programs can access it, researchers can take the first steps towards setting up a functional database.
Another benefit of these AI programs is that they might see connections humans would never have thought of. People can make those leaps, but the chances are much lower, and it takes much longer if it happens at all. Simply put, we’re not capable of processing the sheer amount of data that computers can process.
This isn’t a field where we’re worrying about robots stealing jobs.
Quite the opposite, in fact—we want robots to become commonly-used tools that can help improve patient care and surgical outcomes.
A human surgeon might have intuition, but they’ll never have the steadiness that a pair of robotic hands can provide or the data-processing capabilities of a machine learning algorithm. If we let them, these tools could change the way we look at medicine.
Image Credit: Intuitive Surgical Continue reading

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