Tag Archives: assistant

#434648 The Pediatric AI That Outperformed ...

Training a doctor takes years of grueling work in universities and hospitals. Building a doctor may be as easy as teaching an AI how to read.

Artificial intelligence has taken another step towards becoming an integral part of 21st-century medicine. New research out of Guangzhou, China, published February 11th in Nature Medicine Letters, has demonstrated a natural-language processing AI that is capable of out-performing rookie pediatricians in diagnosing common childhood ailments.

The massive study examined the electronic health records (EHR) from nearly 600,000 patients over an 18-month period at the Guangzhou Women and Children’s Medical Center and then compared AI-generated diagnoses against new assessments from physicians with a range of experience.

The verdict? On average, the AI was noticeably more accurate than junior physicians and nearly as reliable as the more senior ones. These results are the latest demonstration that artificial intelligence is on the cusp of becoming a healthcare staple on a global scale.

Less Like a Computer, More Like a Person
To outshine human doctors, the AI first had to become more human. Like IBM’s Watson, the pediatric AI leverages natural language processing, in essence “reading” written notes from EHRs not unlike how a human doctor would review those same records. But the similarities to human doctors don’t end there. The AI is a machine learning classifier (MLC), capable of placing the information learned from the EHRs into categories to improve performance.

Like traditionally-trained pediatricians, the AI broke cases down into major organ groups and infection areas (upper/lower respiratory, gastrointestinal, etc.) before breaking them down even further into subcategories. It could then develop associations between various symptoms and organ groups and use those associations to improve its diagnoses. This hierarchical approach mimics the deductive reasoning human doctors employ.

Another key strength of the AI developed for this study was the enormous size of the dataset collected to teach it: 1,362,559 outpatient visits from 567,498 patients yielded some 101.6 million data points for the MLC to devour on its quest for pediatric dominance. This allowed the AI the depth of learning needed to distinguish and accurately select from the 55 different diagnosis codes across the various organ groups and subcategories.

When comparing against the human doctors, the study used 11,926 records from an unrelated group of children, giving both the MLC and the 20 humans it was compared against an even playing field. The results were clear: while cohorts of senior pediatricians performed better than the AI, junior pediatricians (those with 3-15 years of experience) were outclassed.

Helping, Not Replacing
While the research used a competitive analysis to measure the success of the AI, the results should be seen as anything but hostile to human doctors. The near future of artificial intelligence in medicine will see these machine learning programs augment, not replace, human physicians. The authors of the study specifically call out augmentation as the key short-term application of their work. Triaging incoming patients via intake forms, performing massive metastudies using EHRs, providing rapid ‘second opinions’—the applications for an AI doctor that is better-but-not-the-best are as varied as the healthcare industry itself.

That’s only considering how artificial intelligence could make a positive impact immediately upon implementation. It’s easy to see how long-term use of a diagnostic assistant could reshape the way modern medical institutions approach their work.

Look at how the MLC results fit snugly between the junior and senior physician groups. Essentially, it took nearly 15 years before a physician could consistently out-diagnose the machine. That’s a decade and a half wherein an AI diagnostic assistant would be an invaluable partner—both as a training tool and a safety measure. Likewise, on the other side of the experience curve you have physicians whose performance could be continuously leveraged to improve the AI’s effectiveness. This is a clear opportunity for a symbiotic relationship, with humans and machines each assisting the other as they mature.

Closer to Us, But Still Dependent on Us
No matter the ultimate application, the AI doctors of the future are drawing nearer to us step by step. This latest research is a demonstration that artificial intelligence can mimic the results of human deductive reasoning even in some of the most complex and important decision-making processes. True, the MLC required input from humans to function; both the initial data points and the cases used to evaluate the AI depended on EHRs written by physicians. While every effort was made to design a test schema that removed any indication of the eventual diagnosis, some “data leakage” is bound to occur.

In other words, when AIs use human-created data, they inherit human insight to some degree. Yet the progress made in machine imaging, chatbots, sensors, and other fields all suggest that this dependence on human input is more about where we are right now than where we could be in the near future.

Data, and More Data
That near future may also have some clear winners and losers. For now, those winners seem to be the institutions that can capture and apply the largest sets of data. With a rapidly digitized society gathering incredible amounts of data, China has a clear advantage. Combined with their relatively relaxed approach to privacy, they are likely to continue as one of the driving forces behind machine learning and its applications. So too will Google/Alphabet with their massive medical studies. Data is the uranium in this AI arms race, and everyone seems to be scrambling to collect more.

In a global community that seems increasingly aware of the potential problems arising from this need for and reliance on data, it’s nice to know there’ll be an upside as well. The technology behind AI medical assistants is looking more and more mature—even if we are still struggling to find exactly where, when, and how that technology should first become universal.

Yet wherever we see the next push to make AI a standard tool in a real-world medical setting, I have little doubt it will greatly improve the lives of human patients. Today Doctor AI is performing as well as a human colleague with more than 10 years of experience. By next year or so, it may take twice as long for humans to be competitive. And in a decade, the combined medical knowledge of all human history may be a tool as common as a stethoscope in your doctor’s hands.

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#434637 AI Is Rapidly Augmenting Healthcare and ...

When it comes to the future of healthcare, perhaps the only technology more powerful than CRISPR is artificial intelligence.

Over the past five years, healthcare AI startups around the globe raised over $4.3 billion across 576 deals, topping all other industries in AI deal activity.

During this same period, the FDA has given 70 AI healthcare tools and devices ‘fast-tracked approval’ because of their ability to save both lives and money.

The pace of AI-augmented healthcare innovation is only accelerating.

In Part 3 of this blog series on longevity and vitality, I cover the different ways in which AI is augmenting our healthcare system, enabling us to live longer and healthier lives.

In this blog, I’ll expand on:

Machine learning and drug design
Artificial intelligence and big data in medicine
Healthcare, AI & China

Let’s dive in.

Machine Learning in Drug Design
What if AI systems, specifically neural networks, could predict the design of novel molecules (i.e. medicines) capable of targeting and curing any disease?

Imagine leveraging cutting-edge artificial intelligence to accomplish with 50 people what the pharmaceutical industry can barely do with an army of 5,000.

And what if these molecules, accurately engineered by AIs, always worked? Such a feat would revolutionize our $1.3 trillion global pharmaceutical industry, which currently holds a dismal record of 1 in 10 target drugs ever reaching human trials.

It’s no wonder that drug development is massively expensive and slow. It takes over 10 years to bring a new drug to market, with costs ranging from $2.5 billion to $12 billion.

This inefficient, slow-to-innovate, and risk-averse industry is a sitting duck for disruption in the years ahead.

One of the hottest startups in digital drug discovery today is Insilico Medicine. Leveraging AI in its end-to-end drug discovery pipeline, Insilico Medicine aims to extend healthy longevity through drug discovery and aging research.

Their comprehensive drug discovery engine uses millions of samples and multiple data types to discover signatures of disease, identify the most promising protein targets, and generate perfect molecules for these targets. These molecules either already exist or can be generated de novo with the desired set of parameters.

In late 2018, Insilico’s CEO Dr. Alex Zhavoronkov announced the groundbreaking result of generating novel molecules for a challenging protein target with an unprecedented hit rate in under 46 days. This included both synthesis of the molecules and experimental validation in a biological test system—an impressive feat made possible by converging exponential technologies.

Underpinning Insilico’s drug discovery pipeline is a novel machine learning technique called Generative Adversarial Networks (GANs), used in combination with deep reinforcement learning.

Generating novel molecular structures for diseases both with and without known targets, Insilico is now pursuing drug discovery in aging, cancer, fibrosis, Parkinson’s disease, Alzheimer’s disease, ALS, diabetes, and many others. Once rolled out, the implications will be profound.

Dr. Zhavoronkov’s ultimate goal is to develop a fully-automated Health-as-a-Service (HaaS) and Longevity-as-a-Service (LaaS) engine.

Once plugged into the services of companies from Alibaba to Alphabet, such an engine would enable personalized solutions for online users, helping them prevent diseases and maintain optimal health.

Insilico, alongside other companies tackling AI-powered drug discovery, truly represents the application of the 6 D’s. What was once a prohibitively expensive and human-intensive process is now rapidly becoming digitized, dematerialized, demonetized and, perhaps most importantly, democratized.

Companies like Insilico can now do with a fraction of the cost and personnel what the pharmaceutical industry can barely accomplish with thousands of employees and a hefty bill to foot.

As I discussed in my blog on ‘The Next Hundred-Billion-Dollar Opportunity,’ Google’s DeepMind has now turned its neural networks to healthcare, entering the digitized drug discovery arena.

In 2017, DeepMind achieved a phenomenal feat by matching the fidelity of medical experts in correctly diagnosing over 50 eye disorders.

And just a year later, DeepMind announced a new deep learning tool called AlphaFold. By predicting the elusive ways in which various proteins fold on the basis of their amino acid sequences, AlphaFold may soon have a tremendous impact in aiding drug discovery and fighting some of today’s most intractable diseases.

Artificial Intelligence and Data Crunching
AI is especially powerful in analyzing massive quantities of data to uncover patterns and insights that can save lives. Take WAVE, for instance. Every year, over 400,000 patients die prematurely in US hospitals as a result of heart attack or respiratory failure.

Yet these patients don’t die without leaving plenty of clues. Given information overload, however, human physicians and nurses alone have no way of processing and analyzing all necessary data in time to save these patients’ lives.

Enter WAVE, an algorithm that can process enough data to offer a six-hour early warning of patient deterioration.

Just last year, the FDA approved WAVE as an AI-based predictive patient surveillance system to predict and thereby prevent sudden death.

Another highly valuable yet difficult-to-parse mountain of medical data comprises the 2.5 million medical papers published each year.

For some time, it has become physically impossible for a human physician to read—let alone remember—all of the relevant published data.

To counter this compounding conundrum, Johnson & Johnson is teaching IBM Watson to read and understand scientific papers that detail clinical trial outcomes.

Enriching Watson’s data sources, Apple is also partnering with IBM to provide access to health data from mobile apps.

One such Watson system contains 40 million documents, ingesting an average of 27,000 new documents per day, and providing insights for thousands of users.

After only one year, Watson’s successful diagnosis rate of lung cancer has reached 90 percent, compared to the 50 percent success rate of human doctors.

But what about the vast amount of unstructured medical patient data that populates today’s ancient medical system? This includes medical notes, prescriptions, audio interview transcripts, and pathology and radiology reports.

In late 2018, Amazon announced a new HIPAA-eligible machine learning service that digests and parses unstructured data into categories, such as patient diagnoses, treatments, dosages, symptoms and signs.

Taha Kass-Hout, Amazon’s senior leader in health care and artificial intelligence, told the Wall Street Journal that internal tests demonstrated that the software even performs as well as or better than other published efforts.

On the heels of this announcement, Amazon confirmed it was teaming up with the Fred Hutchinson Cancer Research Center to evaluate “millions of clinical notes to extract and index medical conditions.”

Having already driven extraordinary algorithmic success rates in other fields, data is the healthcare industry’s goldmine for future innovation.

Healthcare, AI & China
In 2017, the Chinese government published its ambitious national plan to become a global leader in AI research by 2030, with healthcare listed as one of four core research areas during the first wave of the plan.

Just a year earlier, China began centralizing healthcare data, tackling a major roadblock to developing longevity and healthcare technologies (particularly AI systems): scattered, dispersed, and unlabeled patient data.

Backed by the Chinese government, China’s largest tech companies—particularly Tencent—have now made strong entrances into healthcare.

Just recently, Tencent participated in a $154 million megaround for China-based healthcare AI unicorn iCarbonX.

Hoping to develop a complete digital representation of your biological self, iCarbonX has acquired numerous US personalized medicine startups.

Considering Tencent’s own Miying healthcare AI platform—aimed at assisting healthcare institutions in AI-driven cancer diagnostics—Tencent is quickly expanding into the drug discovery space, participating in two multimillion-dollar, US-based AI drug discovery deals just this year.

China’s biggest, second-order move into the healthtech space comes through Tencent’s WeChat. In the course of a mere few years, already 60 percent of the 38,000 medical institutions registered on WeChat allow patients to digitally book appointments through Tencent’s mobile platform. At the same time, 2,000 Chinese hospitals accept WeChat payments.

Tencent has additionally partnered with the U.K.’s Babylon Health, a virtual healthcare assistant startup whose app now allows Chinese WeChat users to message their symptoms and receive immediate medical feedback.

Similarly, Alibaba’s healthtech focus started in 2016 when it released its cloud-based AI medical platform, ET Medical Brain, to augment healthcare processes through everything from diagnostics to intelligent scheduling.

Conclusion
As Nvidia CEO Jensen Huang has stated, “Software ate the world, but AI is going to eat software.” Extrapolating this statement to a more immediate implication, AI will first eat healthcare, resulting in dramatic acceleration of longevity research and an amplification of the human healthspan.

Next week, I’ll continue to explore this concept of AI systems in healthcare.

Particularly, I’ll expand on how we’re acquiring and using the data for these doctor-augmenting AI systems: from ubiquitous biosensors, to the mobile healthcare revolution, and finally, to the transformative power of the health nucleus.

As AI and other exponential technologies increase our healthspan by 30 to 40 years, how will you leverage these same exponential technologies to take on your moonshots and live out your massively transformative purpose?

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#434311 Understanding the Hidden Bias in ...

Facial recognition technology has progressed to point where it now interprets emotions in facial expressions. This type of analysis is increasingly used in daily life. For example, companies can use facial recognition software to help with hiring decisions. Other programs scan the faces in crowds to identify threats to public safety.

Unfortunately, this technology struggles to interpret the emotions of black faces. My new study, published last month, shows that emotional analysis technology assigns more negative emotions to black men’s faces than white men’s faces.

This isn’t the first time that facial recognition programs have been shown to be biased. Google labeled black faces as gorillas. Cameras identified Asian faces as blinking. Facial recognition programs struggled to correctly identify gender for people with darker skin.

My work contributes to a growing call to better understand the hidden bias in artificial intelligence software.

Measuring Bias
To examine the bias in the facial recognition systems that analyze people’s emotions, I used a data set of 400 NBA player photos from the 2016 to 2017 season, because players are similar in their clothing, athleticism, age and gender. Also, since these are professional portraits, the players look at the camera in the picture.

I ran the images through two well-known types of emotional recognition software. Both assigned black players more negative emotional scores on average, no matter how much they smiled.

For example, consider the official NBA pictures of Darren Collison and Gordon Hayward. Both players are smiling, and, according to the facial recognition and analysis program Face++, Darren Collison and Gordon Hayward have similar smile scores—48.7 and 48.1 out of 100, respectively.

Basketball players Darren Collision (left) and Gordon Hayward (right). basketball-reference.com

However, Face++ rates Hayward’s expression as 59.7 percent happy and 0.13 percent angry and Collison’s expression as 39.2 percent happy and 27 percent angry. Collison is viewed as nearly as angry as he is happy and far angrier than Hayward—despite the facial recognition program itself recognizing that both players are smiling.

In contrast, Microsoft’s Face API viewed both men as happy. Still, Collison is viewed as less happy than Hayward, with 98 and 93 percent happiness scores, respectively. Despite his smile, Collison is even scored with a small amount of contempt, whereas Hayward has none.

Across all the NBA pictures, the same pattern emerges. On average, Face++ rates black faces as twice as angry as white faces. Face API scores black faces as three times more contemptuous than white faces. After matching players based on their smiles, both facial analysis programs are still more likely to assign the negative emotions of anger or contempt to black faces.

Stereotyped by AI
My study shows that facial recognition programs exhibit two distinct types of bias.

First, black faces were consistently scored as angrier than white faces for every smile. Face++ showed this type of bias. Second, black faces were always scored as angrier if there was any ambiguity about their facial expression. Face API displayed this type of disparity. Even if black faces are partially smiling, my analysis showed that the systems assumed more negative emotions as compared to their white counterparts with similar expressions. The average emotional scores were much closer across races, but there were still noticeable differences for black and white faces.

This observation aligns with other research, which suggests that black professionals must amplify positive emotions to receive parity in their workplace performance evaluations. Studies show that people perceive black men as more physically threatening than white men, even when they are the same size.

Some researchers argue that facial recognition technology is more objective than humans. But my study suggests that facial recognition reflects the same biases that people have. Black men’s facial expressions are scored with emotions associated with threatening behaviors more often than white men, even when they are smiling. There is good reason to believe that the use of facial recognition could formalize preexisting stereotypes into algorithms, automatically embedding them into everyday life.

Until facial recognition assesses black and white faces similarly, black people may need to exaggerate their positive facial expressions—essentially smile more—to reduce ambiguity and potentially negative interpretations by the technology.

Although innovative, artificial intelligence can perpetrate and exacerbate existing power dynamics, leading to disparate impact across racial/ethnic groups. Some societal accountability is necessary to ensure fairness to all groups because facial recognition, like most artificial intelligence, is often invisible to the people most affected by its decisions.

Lauren Rhue, Assistant Professor of Information Systems and Analytics, Wake Forest University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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#433954 The Next Great Leap Forward? Combining ...

The Internet of Things is a popular vision of objects with internet connections sending information back and forth to make our lives easier and more comfortable. It’s emerging in our homes, through everything from voice-controlled speakers to smart temperature sensors. To improve our fitness, smart watches and Fitbits are telling online apps how much we’re moving around. And across entire cities, interconnected devices are doing everything from increasing the efficiency of transport to flood detection.

In parallel, robots are steadily moving outside the confines of factory lines. They’re starting to appear as guides in shopping malls and cruise ships, for instance. As prices fall and the artificial intelligence (AI) and mechanical technology continues to improve, we will get more and more used to them making independent decisions in our homes, streets and workplaces.

Here lies a major opportunity. Robots become considerably more capable with internet connections. There is a growing view that the next evolution of the Internet of Things will be to incorporate them into the network, opening up thrilling possibilities along the way.

Home Improvements
Even simple robots become useful when connected to the internet—getting updates about their environment from sensors, say, or learning about their users’ whereabouts and the status of appliances in the vicinity. This lets them lend their bodies, eyes, and ears to give an otherwise impersonal smart environment a user-friendly persona. This can be particularly helpful for people at home who are older or have disabilities.

We recently unveiled a futuristic apartment at Heriot-Watt University to work on such possibilities. One of a few such test sites around the EU, our whole focus is around people with special needs—and how robots can help them by interacting with connected devices in a smart home.

Suppose a doorbell rings that has smart video features. A robot could find the person in the home by accessing their location via sensors, then tell them who is at the door and why. Or it could help make video calls to family members or a professional carer—including allowing them to make virtual visits by acting as a telepresence platform.

Equally, it could offer protection. It could inform them the oven has been left on, for example—phones or tablets are less reliable for such tasks because they can be misplaced or not heard.

Similarly, the robot could raise the alarm if its user appears to be in difficulty.Of course, voice-assistant devices like Alexa or Google Home can offer some of the same services. But robots are far better at moving, sensing and interacting with their environment. They can also engage their users by pointing at objects or acting more naturally, using gestures or facial expressions. These “social abilities” create bonds which are crucially important for making users more accepting of the support and making it more effective.

To help incentivize the various EU test sites, our apartment also hosts the likes of the European Robotic League Service Robot Competition—a sort of Champions League for robots geared to special needs in the home. This brought academics from around Europe to our laboratory for the first time in January this year. Their robots were tested in tasks like welcoming visitors to the home, turning the oven off, and fetching objects for their users; and a German team from Koblenz University won with a robot called Lisa.

Robots Offshore
There are comparable opportunities in the business world. Oil and gas companies are looking at the Internet of Things, for example; experimenting with wireless sensors to collect information such as temperature, pressure, and corrosion levels to detect and possibly predict faults in their offshore equipment.

In the future, robots could be alerted to problem areas by sensors to go and check the integrity of pipes and wells, and to make sure they are operating as efficiently and safely as possible. Or they could place sensors in parts of offshore equipment that are hard to reach, or help to calibrate them or replace their batteries.

The likes of the ORCA Hub, a £36m project led by the Edinburgh Centre for Robotics, bringing together leading experts and over 30 industry partners, is developing such systems. The aim is to reduce the costs and the risks of humans working in remote hazardous locations.

ORCA tests a drone robot. ORCA
Working underwater is particularly challenging, since radio waves don’t move well under the sea. Underwater autonomous vehicles and sensors usually communicate using acoustic waves, which are many times slower (1,500 meters a second vs. 300m meters a second for radio waves). Acoustic communication devices are also much more expensive than those used above the water.

This academic project is developing a new generation of low-cost acoustic communication devices, and trying to make underwater sensor networks more efficient. It should help sensors and underwater autonomous vehicles to do more together in future—repair and maintenance work similar to what is already possible above the water, plus other benefits such as helping vehicles to communicate with one another over longer distances and tracking their location.

Beyond oil and gas, there is similar potential in sector after sector. There are equivalents in nuclear power, for instance, and in cleaning and maintaining the likes of bridges and buildings. My colleagues and I are also looking at possibilities in areas such as farming, manufacturing, logistics, and waste.

First, however, the research sectors around the Internet of Things and robotics need to properly share their knowledge and expertise. They are often isolated from one another in different academic fields. There needs to be more effort to create a joint community, such as the dedicated workshops for such collaboration that we organized at the European Robotics Forum and the IoT Week in 2017.

To the same end, industry and universities need to look at setting up joint research projects. It is particularly important to address safety and security issues—hackers taking control of a robot and using it to spy or cause damage, for example. Such issues could make customers wary and ruin a market opportunity.

We also need systems that can work together, rather than in isolated applications. That way, new and more useful services can be quickly and effectively introduced with no disruption to existing ones. If we can solve such problems and unite robotics and the Internet of Things, it genuinely has the potential to change the world.

Mauro Dragone, Assistant Professor, Cognitive Robotics, Multiagent systems, Internet of Things, Heriot-Watt University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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#433939 The Promise—and Complications—of ...

Every year, for just a few days in a major city, a small team of roboticists get to live the dream: ordering around their own personal robot butlers. In carefully-constructed replicas of a restaurant scene or a domestic setting, these robots perform any number of simple algorithmic tasks. “Get the can of beans from the shelf. Greet the visitors to the museum. Help the humans with their shopping. Serve the customers at the restaurant.”

This is Robocup @ Home, the annual tournament where teams of roboticists put their autonomous service robots to the test for practical domestic applications. The tasks seem simple and mundane, but considering the technology required reveals that they’re really not.

The Robot Butler Contest
Say you want a robot to fetch items in the supermarket. In a crowded, noisy environment, the robot must understand your commands, ask for clarification, and map out and navigate an unfamiliar environment, avoiding obstacles and people as it does so. Then it must recognize the product you requested, perhaps in a cluttered environment, perhaps in an unfamiliar orientation. It has to grasp that product appropriately—recall that there are entire multi-million-dollar competitions just dedicated to developing robots that can grasp a range of objects—and then return it to you.

It’s a job so simple that a child could do it—and so complex that teams of smart roboticists can spend weeks programming and engineering, and still end up struggling to complete simplified versions of this task. Of course, the child has the advantage of millions of years of evolutionary research and development, while the first robots that could even begin these tasks were only developed in the 1970s.

Even bearing this in mind, Robocup @ Home can feel like a place where futurist expectations come crashing into technologist reality. You dream of a smooth-voiced, sardonic JARVIS who’s already made your favorite dinner when you come home late from work; you end up shouting “remember the biscuits” at a baffled, ungainly droid in aisle five.

Caring for the Elderly
Famously, Japan is one of the most robo-enthusiastic nations in the world; they are the nation that stunned us all with ASIMO in 2000, and several studies have been conducted into the phenomenon. It’s no surprise, then, that humanoid robotics should be seriously considered as a solution to the crisis of the aging population. The Japanese government, as part of its robots strategy, has already invested $44 million in their development.

Toyota’s Human Support Robot (HSR-2) is a simple but programmable robot with a single arm; it can be remote-controlled to pick up objects and can monitor patients. HSR-2 has become the default robot for use in Robocup @ Home tournaments, at least in tasks that involve manipulating objects.

Alongside this, Toyota is working on exoskeletons to assist people in walking after strokes. It may surprise you to learn that nurses suffer back injuries more than any other occupation, at roughly three times the rate of construction workers, due to the day-to-day work of lifting patients. Toyota has a Care Assist robot/exoskeleton designed to fix precisely this problem by helping care workers with the heavy lifting.

The Home of the Future
The enthusiasm for domestic robotics is easy to understand and, in fact, many startups already sell robots marketed as domestic helpers in some form or another. In general, though, they skirt the immensely complicated task of building a fully capable humanoid robot—a task that even Google’s skunk-works department gave up on, at least until recently.

It’s plain to see why: far more research and development is needed before these domestic robots could be used reliably and at a reasonable price. Consumers with expectations inflated by years of science fiction saturation might find themselves frustrated as the robots fail to perform basic tasks.

Instead, domestic robotics efforts fall into one of two categories. There are robots specialized to perform a domestic task, like iRobot’s Roomba, which stuck to vacuuming and became the most successful domestic robot of all time by far.

The tasks need not necessarily be simple, either: the impressive but expensive automated kitchen uses the world’s most dexterous hands to cook meals, providing it can recognize the ingredients. Other robots focus on human-robot interaction, like Jibo: they essentially package the abilities of a voice assistant like Siri, Cortana, or Alexa to respond to simple questions and perform online tasks in a friendly, dynamic robot exterior.

In this way, the future of domestic automation starts to look a lot more like smart homes than a robot or domestic servant. General robotics is difficult in the same way that general artificial intelligence is difficult; competing with humans, the great all-rounders, is a challenge. Getting superhuman performance at a more specific task, however, is feasible and won’t cost the earth.

Individual startups without the financial might of a Google or an Amazon can develop specialized robots, like Seven Dreamers’ laundry robot, and hope that one day it will form part of a network of autonomous robots that each have a role to play in the household.

Domestic Bliss?
The Smart Home has been a staple of futurist expectations for a long time, to the extent that movies featuring smart homes out of control are already a cliché. But critics of the smart home idea—and of the internet of things more generally—tend to focus on the idea that, more often than not, software just adds an additional layer of things that can break (NSFW), in exchange for minimal added convenience. A toaster that can short-circuit is bad enough, but a toaster that can refuse to serve you toast because its firmware is updating is something else entirely.

That’s before you even get into the security vulnerabilities, which are all the more important when devices are installed in your home and capable of interacting with them. The idea of a smart watch that lets you keep an eye on your children might sound like something a security-conscious parent would like: a smart watch that can be hacked to track children, listen in on their surroundings, and even fool them into thinking a call is coming from their parents is the stuff of nightmares.

Key to many of these problems is the lack of standardization for security protocols, and even the products themselves. The idea of dozens of startups each developing a highly-specialized piece of robotics to perform a single domestic task sounds great in theory, until you realize the potential hazards and pitfalls of getting dozens of incompatible devices to work together on the same system.

It seems inevitable that there are yet more layers of domestic drudgery that can be automated away, decades after the first generation of time-saving domestic devices like the dishwasher and vacuum cleaner became mainstream. With projected market values into the billions and trillions of dollars, there is no shortage of industry interest in ironing out these kinks. But, for now at least, the answer to the question: “Where’s my robot butler?” is that it is gradually, painstakingly learning how to sort through groceries.

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