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Robots have been masters of manufacturing at speed and precision for decades, but give them a seemingly simple task like stacking shelves, and they quickly get stuck. That’s changing, though, as engineers build systems that can take on the deceptively tricky tasks most humans can do with their eyes closed.
Boston Dynamics is famous for dramatic reveals of robots performing mind-blowing feats that also leave you scratching your head as to what the market is—think the bipedal Atlas doing backflips or Spot the galloping robot dog.
Last week, the company released a video of a robot called Handle that looks like an ostrich on wheels carrying out the seemingly mundane task of stacking boxes in a warehouse.
It might seem like a step backward, but this is exactly the kind of practical task robots have long struggled with. While the speed and precision of industrial robots has seen them take over many functions in modern factories, they’re generally limited to highly prescribed tasks carried out in meticulously-controlled environments.
That’s because despite their mechanical sophistication, most are still surprisingly dumb. They can carry out precision welding on a car or rapidly assemble electronics, but only by rigidly following a prescribed set of motions. Moving cardboard boxes around a warehouse might seem simple to a human, but it actually involves a variety of tasks machines still find pretty difficult—perceiving your surroundings, navigating, and interacting with objects in a dynamic environment.
But the release of this video suggests Boston Dynamics thinks these kinds of applications are close to prime time. Last week the company doubled down by announcing the acquisition of start-up Kinema Systems, which builds computer vision systems for robots working in warehouses.
It’s not the only company making strides in this area. On the same day the video went live, Google unveiled a robot arm called TossingBot that can pick random objects from a box and quickly toss them into another container beyond its reach, which could prove very useful for sorting items in a warehouse. The machine can train on new objects in just an hour or two, and can pick and toss up to 500 items an hour with better accuracy than any of the humans who tried the task.
And an apple-picking robot built by Abundant Robotics is currently on New Zealand farms navigating between rows of apple trees using LIDAR and computer vision to single out ripe apples before using a vacuum tube to suck them off the tree.
In most cases, advances in machine learning and computer vision brought about by the recent AI boom are the keys to these rapidly improving capabilities. Robots have historically had to be painstakingly programmed by humans to solve each new task, but deep learning is making it possible for them to quickly train themselves on a variety of perception, navigation, and dexterity tasks.
It’s not been simple, though, and the application of deep learning in robotics has lagged behind other areas. A major limitation is that the process typically requires huge amounts of training data. That’s fine when you’re dealing with image classification, but when that data needs to be generated by real-world robots it can make the approach impractical. Simulations offer the possibility to run this training faster than real time, but it’s proved difficult to translate policies learned in virtual environments into the real world.
Recent years have seen significant progress on these fronts, though, and the increasing integration of modern machine learning with robotics. In October, OpenAI imbued a robotic hand with human-level dexterity by training an algorithm in a simulation using reinforcement learning before transferring it to the real-world device. The key to ensuring the translation went smoothly was injecting random noise into the simulation to mimic some of the unpredictability of the real world.
And just a couple of weeks ago, MIT researchers demonstrated a new technique that let a robot arm learn to manipulate new objects with far less training data than is usually required. By getting the algorithm to focus on a few key points on the object necessary for picking it up, the system could learn to pick up a previously unseen object after seeing only a few dozen examples (rather than the hundreds or thousands typically required).
How quickly these innovations will trickle down to practical applications remains to be seen, but a number of startups as well as logistics behemoth Amazon are developing robots designed to flexibly pick and place the wide variety of items found in your average warehouse.
Whether the economics of using robots to replace humans at these kinds of menial tasks makes sense yet is still unclear. The collapse of collaborative robotics pioneer Rethink Robotics last year suggests there are still plenty of challenges.
But at the same time, the number of robotic warehouses is expected to leap from 4,000 today to 50,000 by 2025. It may not be long until robots are muscling in on tasks we’ve long assumed only humans could do.
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As over-hyped as artificial intelligence is—everyone’s talking about it, few fully understand it, it might leave us all unemployed but also solve all the world’s problems—its list of accomplishments is growing. AI can now write realistic-sounding text, give a debating champ a run for his money, diagnose illnesses, and generate fake human faces—among much more.
After training these systems on massive datasets, their creators essentially just let them do their thing to arrive at certain conclusions or outcomes. The problem is that more often than not, even the creators don’t know exactly why they’ve arrived at those conclusions or outcomes. There’s no easy way to trace a machine learning system’s rationale, so to speak. The further we let AI go down this opaque path, the more likely we are to end up somewhere we don’t want to be—and may not be able to come back from.
In a panel at the South by Southwest interactive festival last week titled “Ethics and AI: How to plan for the unpredictable,” experts in the field shared their thoughts on building more transparent, explainable, and accountable AI systems.
Not New, but Different
Ryan Welsh, founder and director of explainable AI startup Kyndi, pointed out that having knowledge-based systems perform advanced tasks isn’t new; he cited logistical, scheduling, and tax software as examples. What’s new is the learning component, our inability to trace how that learning occurs, and the ethical implications that could result.
“Now we have these systems that are learning from data, and we’re trying to understand why they’re arriving at certain outcomes,” Welsh said. “We’ve never actually had this broad society discussion about ethics in those scenarios.”
Rather than continuing to build AIs with opaque inner workings, engineers must start focusing on explainability, which Welsh broke down into three subcategories. Transparency and interpretability come first, and refer to being able to find the units of high influence in a machine learning network, as well as the weights of those units and how they map to specific data and outputs.
Then there’s provenance: knowing where something comes from. In an ideal scenario, for example, Open AI’s new text generator would be able to generate citations in its text that reference academic (and human-created) papers or studies.
Explainability itself is the highest and final bar and refers to a system’s ability to explain itself in natural language to the average user by being able to say, “I generated this output because x, y, z.”
“Humans are unique in our ability and our desire to ask why,” said Josh Marcuse, executive director of the Defense Innovation Board, which advises Department of Defense senior leaders on innovation. “The reason we want explanations from people is so we can understand their belief system and see if we agree with it and want to continue to work with them.”
Similarly, we need to have the ability to interrogate AIs.
Two Types of Thinking
Welsh explained that one big barrier standing in the way of explainability is the tension between the deep learning community and the symbolic AI community, which see themselves as two different paradigms and historically haven’t collaborated much.
Symbolic or classical AI focuses on concepts and rules, while deep learning is centered around perceptions. In human thought this is the difference between, for example, deciding to pass a soccer ball to a teammate who is open (you make the decision because conceptually you know that only open players can receive passes), and registering that the ball is at your feet when someone else passes it to you (you’re taking in information without making a decision about it).
“Symbolic AI has abstractions and representation based on logic that’s more humanly comprehensible,” Welsh said. To truly mimic human thinking, AI needs to be able to both perceive information and conceptualize it. An example of perception (deep learning) in an AI is recognizing numbers within an image, while conceptualization (symbolic learning) would give those numbers a hierarchical order and extract rules from the hierachy (4 is greater than 3, and 5 is greater than 4, therefore 5 is also greater than 3).
Explainability comes in when the system can say, “I saw a, b, and c, and based on that decided x, y, or z.” DeepMind and others have recently published papers emphasizing the need to fuse the two paradigms together.
Implications Across Industries
One of the most prominent fields where AI ethics will come into play, and where the transparency and accountability of AI systems will be crucial, is defense. Marcuse said, “We’re accountable beings, and we’re responsible for the choices we make. Bringing in tech or AI to a battlefield doesn’t strip away that meaning and accountability.”
In fact, he added, rather than worrying about how AI might degrade human values, people should be asking how the tech could be used to help us make better moral choices.
It’s also important not to conflate AI with autonomy—a worst-case scenario that springs to mind is an intelligent destructive machine on a rampage. But in fact, Marcuse said, in the defense space, “We have autonomous systems today that don’t rely on AI, and most of the AI systems we’re contemplating won’t be autonomous.”
The US Department of Defense released its 2018 artificial intelligence strategy last month. It includes developing a robust and transparent set of principles for defense AI, investing in research and development for AI that’s reliable and secure, continuing to fund research in explainability, advocating for a global set of military AI guidelines, and finding ways to use AI to reduce the risk of civilian casualties and other collateral damage.
Though these were designed with defense-specific aims in mind, Marcuse said, their implications extend across industries. “The defense community thinks of their problems as being unique, that no one deals with the stakes and complexity we deal with. That’s just wrong,” he said. Making high-stakes decisions with technology is widespread; safety-critical systems are key to aviation, medicine, and self-driving cars, to name a few.
Marcuse believes the Department of Defense can invest in AI safety in a way that has far-reaching benefits. “We all depend on technology to keep us alive and safe, and no one wants machines to harm us,” he said.
A Creation Superior to Its Creator
That said, we’ve come to expect technology to meet our needs in just the way we want, all the time—servers must never be down, GPS had better not take us on a longer route, Google must always produce the answer we’re looking for.
With AI, though, our expectations of perfection may be less reasonable.
“Right now we’re holding machines to superhuman standards,” Marcuse said. “We expect them to be perfect and infallible.” Take self-driving cars. They’re conceived of, built by, and programmed by people, and people as a whole generally aren’t great drivers—just look at traffic accident death rates to confirm that. But the few times self-driving cars have had fatal accidents, there’s been an ensuing uproar and backlash against the industry, as well as talk of implementing more restrictive regulations.
This can be extrapolated to ethics more generally. We as humans have the ability to explain our decisions, but many of us aren’t very good at doing so. As Marcuse put it, “People are emotional, they confabulate, they lie, they’re full of unconscious motivations. They don’t pass the explainability test.”
Why, then, should explainability be the standard for AI?
Even if humans aren’t good at explaining our choices, at least we can try, and we can answer questions that probe at our decision-making process. A deep learning system can’t do this yet, so working towards being able to identify which input data the systems are triggering on to make decisions—even if the decisions and the process aren’t perfect—is the direction we need to head.
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More than 700,000 Multiple Launch Rocket System submunitions have been demilitarized since the Army started using an automated nine-robot system conceptualized, built and programmed by Sandia National Laboratories engineers. Continue reading
The causes of aging are extremely complex and unclear. With the dramatic demonetization of genome reading and editing over the past decade, and Big Pharma, startups, and the FDA starting to face aging as a disease, we are starting to find practical ways to extend our healthspan.
Here, in Part 2 of a series of blogs on longevity and vitality, I explore how genome sequencing and editing, along with new classes of anti-aging drugs, are augmenting our biology to further extend our healthy lives.
In this blog I’ll cover two classes of emerging technologies:
Genome Sequencing and Editing;
Senolytics, Nutraceuticals & Pharmaceuticals.
Let’s dive in.
Genome Sequencing & Editing
Your genome is the software that runs your body.
A sequence of 3.2 billion letters makes you “you.” These base pairs of A’s, T’s, C’s, and G’s determine your hair color, your height, your personality, your propensity to disease, your lifespan, and so on.
Until recently, it’s been very difficult to rapidly and cheaply “read” these letters—and even more difficult to understand what they mean.
Since 2001, the cost to sequence a whole human genome has plummeted exponentially, outpacing Moore’s Law threefold. From an initial cost of $3.7 billion, it dropped to $10 million in 2006, and to $5,000 in 2012.
Today, the cost of genome sequencing has dropped below $500, and according to Illumina, the world’s leading sequencing company, the process will soon cost about $100 and take about an hour to complete.
This represents one of the most powerful and transformative technology revolutions in healthcare.
When we understand your genome, we’ll be able to understand how to optimize “you.”
We’ll know the perfect foods, the perfect drugs, the perfect exercise regimen, and the perfect supplements, just for you.
We’ll understand what microbiome types, or gut flora, are ideal for you (more on this in a later blog).
We’ll accurately predict how specific sedatives and medicines will impact you.
We’ll learn which diseases and illnesses you’re most likely to develop and, more importantly, how to best prevent them from developing in the first place (rather than trying to cure them after the fact).
CRISPR Gene Editing
In addition to reading the human genome, scientists can now edit a genome using a naturally-occurring biological system discovered in 1987 called CRISPR/Cas9.
Short for Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR-associated protein 9, the editing system was adapted from a naturally-occurring defense system found in bacteria.
Here’s how it works:
The bacteria capture snippets of DNA from invading viruses (or bacteriophage) and use them to create DNA segments known as CRISPR arrays.
The CRISPR arrays allow the bacteria to “remember” the viruses (or closely related ones), and defend against future invasions.
If the viruses attack again, the bacteria produce RNA segments from the CRISPR arrays to target the viruses’ DNA. The bacteria then use Cas9 to cut the DNA apart, which disables the virus.
Most importantly, CRISPR is cheap, quick, easy to use, and more accurate than all previous gene editing methods. As a result, CRISPR/Cas9 has swept through labs around the world as the way to edit a genome.
A short search in the literature will show an exponential rise in the number of CRISPR-related publications and patents.
2018: Filled With CRISPR Breakthroughs
Early results are impressive. Researchers from the University of Chicago recently used CRISPR to genetically engineer cocaine resistance into mice.
Researchers at the University of Texas Southwestern Medical Center used CRISPR to reverse the gene defect causing Duchenne muscular dystrophy (DMD) in dogs (DMD is the most common fatal genetic disease in children).
With great power comes great responsibility, and moral and ethical dilemmas.
In 2015, Chinese scientists sparked global controversy when they first edited human embryo cells in the lab with the goal of modifying genes that would make the child resistant to smallpox, HIV, and cholera.
Three years later, in November 2018, researcher He Jiankui informed the world that the first set of CRISPR-engineered female twins had been delivered.
To accomplish his goal, Jiankui deleted a region of a receptor on the surface of white blood cells known as CCR5, introducing a rare, natural genetic variation that makes it more difficult for HIV to infect its favorite target, white blood cells.
Setting aside the significant ethical conversations, CRISPR will soon provide us the tools to eliminate diseases, create hardier offspring, produce new environmentally resistant crops, and even wipe out pathogens.
Senolytics, Nutraceuticals & Pharmaceuticals
Over the arc of your life, the cells in your body divide until they reach what is known as the Hayflick limit, or the number of times a normal human cell population will divide before cell division stops, which is typically about 50 divisions.
What normally follows next is programmed cell death or destruction by the immune system. A very small fraction of cells, however, become senescent cells and evade this fate to linger indefinitely.
These lingering cells secrete a potent mix of molecules that triggers chronic inflammation, damages the surrounding tissue structures, and changes the behavior of nearby cells for the worse.
Senescent cells appear to be one of the root causes of aging, causing everything from fibrosis and blood vessel calcification, to localized inflammatory conditions such as osteoarthritis, to diminished lung function.
Fortunately, both the scientific and entrepreneurial communities have begun to work on senolytic therapies, moving the technology for selectively destroying senescent cells out of the laboratory and into a half-dozen startup companies.
Prominent companies in the field include the following:
Unity Biotechnology is developing senolytic medicines to selectively eliminate senescent cells with an initial focus on delivering localized therapy in osteoarthritis, ophthalmology and pulmonary disease.
Oisin Biotechnologiesis pioneering a programmable gene therapy that can destroy cells based on their internal biochemistry.
SIWA Therapeuticsis working on an immunotherapy approach to the problem of senescent cells.
In recent years, researchers have identified or designed a handful of senolytic compounds that can curb aging by regulating senescent cells. Two of these drugs that have gained mainstay research traction are rapamycin and metformin.
Originally extracted from bacteria found on Easter Island, Rapamycin acts on the m-TOR (mechanistic target of rapamycin) pathway to selectively block a key protein that facilitates cell division.
Currently, rapamycin derivatives are widely used as immunosuppression in organ and bone marrow transplants. Research now suggests that use results in prolonged lifespan and enhanced cognitive and immune function.
PureTech Health subsidiary resTORbio (which started 2018 by going public) is working on a rapamycin-based drug intended to enhance immunity and reduce infection. Their clinical-stage RTB101 drug works by inhibiting part of the mTOR pathway.
Results of the drug’s recent clinical trial include:
Decreased incidence of infection
Improved influenza vaccination response
A 30.6 percent decrease in respiratory tract infections
Impressive, to say the least.
Metformin is a widely-used generic drug for mitigating liver sugar production in Type 2 diabetes patients.
Researchers have found that Metformin also reduces oxidative stress and inflammation, which otherwise increase as we age.
There is strong evidence that Metformin can augment cellular regeneration and dramatically mitigate cellular senescence by reducing both oxidative stress and inflammation.
Over 100 studies registered on ClinicalTrials.gov are currently following up on strong evidence of Metformin’s protective effect against cancer.
Nutraceuticals and NAD+
Beyond cellular senescence, certain critical nutrients and proteins tend to decline as a function of age. Nutraceuticals combat aging by supplementing and replenishing these declining nutrient levels.
NAD+ exists in every cell, participating in every process from DNA repair to creating the energy vital for cellular processes. It’s been shown that NAD+ levels decline as we age.
The Elysium Health Basis supplement aims to elevate NAD+ levels in the body to extend one’s lifespan. Elysium’s clinical study reports that Basis increases NAD+ levels consistently by a sustained 40 percent.
These are just a taste of the tremendous momentum that longevity and aging technology has right now. As artificial intelligence and quantum computing transform how we decode our DNA and how we discover drugs, genetics and pharmaceuticals will become truly personalized.
The next blog in this series will demonstrate how artificial intelligence is converging with genetics and pharmaceuticals to transform how we approach longevity, aging, and vitality.
We are edging closer to a dramatically extended healthspan—where 100 is the new 60. What will you create, where will you explore, and how will you spend your time if you are able to add an additional 40 healthy years to your life?
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