Childhood — and parenting — have radically changed in the past few decades, to the point where far more children today struggle to manage their behavior.
That’s the argument Katherine Reynolds Lewis makes in her new parenting book, The Good News About Bad Behavior.
“We face a crisis of self-regulation,” Lewis writes. And by “we,” she means parents and teachers who struggle daily with difficult behavior from the children in their lives.
Lewis, a journalist, certified parent educator and mother of three, asks why so many kids today are having trouble managing their behavior and emotions.
Three factors, she says, have contributed mightily to this crisis.
First: Where, how and how much kids are allowed to play has changed. Second, their access to technology and social media has exploded.
Finally, Lewis suggests, children today are too “unemployed.” She doesn’t simply mean the occasional summer job for a high school teen. The term is a big tent, and she uses it to include household jobs that can help even toddlers build confidence and a sense of community.
“They’re not asked to do anything to contribute to a neighborhood or family or community,” Lewis tells NPR in a recent interview. “And that really erodes their sense of self-worth — just as it would with an adult being unemployed.”
Below is more of that interview, edited for length and clarity.
What sorts of tasks are children and parents prioritizing instead of household responsibilities?
To be straight-A students and athletic superstars, gifted musicians and artists — which are all wonderful goals, but they are long-term and pretty narcissistic. They don’t have that sense of contribution and belonging in a family the way that a simple household chore does, like helping a parent prepare a meal. Anyone who loves to cook knows it’s so satisfying to feed someone you love and to see that gratitude and enjoyment on their faces. And kids today are robbed of that.
It’s part of the work of the family. We all do it, and when it’s more of a social compact than an adult in charge of doling out a reward, that’s much more powerful. They can see that everyone around them is doing jobs. So it seems only fair that they should also.
Kids are so driven by what’s fair and what’s unfair. And that’s why the more power you give kids, the more control you give them, the more they will step up.
You also argue that play has changed dramatically. How so?
Two or three decades ago, children were roaming neighborhoods in mixed-age groups, playing pretty unsupervised or lightly supervised. They were able to resolve disputes, which they had a strong motivation to because they wanted to keep playing. They also planned their time and managed their games. They had a lot of autonomy, which also feeds self-esteem and mental health.
Nowadays, kids, including my own, are in child care pretty much from morning until they fall into bed — or they’re under the supervision of their parents. So they aren’t taking small risks. They aren’t managing their time. They aren’t making decisions and resolving disputes with their playmates the way that kids were 20 or 30 years ago. And those are really important social and emotional skills for kids to learn, and play is how all young mammals learn them.
While we’re on the subject of play and the importance of letting kids take risks, even physical risks, you mention a remarkable study out of New Zealand — about phobias. Can you tell us about it?
This study dates back to when psychologists believed that if you had a phobia as an adult, you must have had some traumatic experience as a child. So they started looking at people who had phobias and what their childhood experiences were like. In fact, they found the opposite relationship.
People who had a fall from heights were less likely to have an adult phobia of heights. People who had an early experience with near-drowning had zero correlation with a phobia of water, and children who were separated from their parents briefly at an early age actually had less separation anxiety later in life.
We need to help kids to develop tolerance against anxiety, and the best way to do that, this research suggests, is to take small risks — to have falls and scrapes and tumbles and discover that they’re capable and that they can survive being hurt. Let them play with sticks or fall off a tree. And yeah, maybe they break their arm, but that’s how they learn how high they can climb.
You say in the book that “we face a crisis of self-regulation.” What does that look like at home and in the classroom?
It’s the behavior in our homes that keeps us from getting out the door in the morning and keeps us from getting our kids to sleep at night.
In schools, it’s kids jumping out of seats because they can’t control their behavior or their impulses, getting into shoving matches on the playground, being frozen during tests because they have such high rates of anxiety.
Really, I lump under this umbrella of self-regulation the increase in anxiety, depression, ADHD, substance addiction and all of these really big challenges that are ways kids are trying to manage their thoughts, behavior and emotions because they don’t have the other skills to do it in healthy ways.
You write a lot about the importance of giving kids a sense of control. My 6-year-old resists our morning schedule, from waking up to putting on his shoes. Where is the middle ground between giving him control over his choices and making sure he’s ready when it’s time to go?
It’s a really tough balance. We start off, when our kids are babies, being in charge of everything. And our goal by the time they’re 18 is to be in charge of nothing — to work ourselves out of the job of being that controlling parent. So we have to constantly be widening the circle of things that they’re in charge of, and shrinking our own responsibility.
It’s a bit of a dance for a 6-year-old, really. They love power. So give him as much power as you can stand and really try to save your direction for the things that you don’t think he can do.
He knows how to put on his shoes. So if you walk out the door, he will put on his shoes and follow you. It may not feel like it, but eventually he will. And if you spend five or 10 minutes outside that door waiting for him — not threatening or nagging — he’ll be more likely to do it quickly. It’s one of these things that takes a leap of faith, but it really works.
Kids also love to be part of that discussion of, what does the morning look like. Does he want to draw a visual calendar of the things that he wants to get done in the morning? Does he want to set times, or, if he’s done by a certain time, does he get to do something fun before you leave the house? All those things that are his ideas will pull him into the routine and make him more willing to cooperate.
Whether you’re trying to get your child to dress, do homework or practice piano, it’s tempting to use rewards that we know our kids love, especially sweets and screen time. You argue in the book: Be careful. Why?
Yes. The research on rewards is pretty powerful, and it suggests that the more we reward behavior, the less desirable that behavior becomes to children and adults alike. If the child is coming up with, “Oh, I’d really like to do this,” and it stems from his intrinsic interests and he’s more in charge of it, then it becomes less of a bribe and more of a way that he’s structuring his own morning.
The adult doling out rewards is really counterproductive in the long term — even though they may seem to work in the short term. The way parents or teachers discover this is that they stop working. At some point, the kid says, “I don’t really care about your reward. I’m going to do what I want.” And then we have no tools. Instead, we use strategies that are built on mutual respect and a mutual desire to get through the day smoothly.
You offer pretty simple guidance for parents when they’re confronted with misbehavior and feel they need to dole out consequences. You call them the four R’s. Can you walk me through them?
The four R’s will keep a consequence from becoming a punishment. So it’s important to avoid power struggles and to win the kid’s cooperation. They are: Any consequence should be revealed in advance, respectful, related to the decision the child made, and reasonable in scope.
Generally, by the time they’re 6 or 7 years old, kids know the rules of society and politeness, and we don’t need to give them a lecture in that moment of misbehavior to drill it into their heads. In fact, acting in that moment can sometimes be counterproductive if they are amped up, their amygdala’s activated, they’re in a tantrum or excited state, and they can’t really learn very well because they can’t access the problem-solving part of their brain, the prefrontal cortex, where they’re really making decisions and thinking rationally. So every misbehavior doesn’t need an immediate consequence.
You even tell parents, in the heat of the moment, it’s OK to just mumble and walk away. What do you mean?
That’s when you are looking at your child, they are not doing what you want, and you cannot think of what to do. Instead of jumping in with a bribe or a punishment or yelling, you give yourself some space. Pretend you had something on the stove you need to grab or that you hear something ringing in the other room and walk away. That gives you just a little space to gather your thoughts and maybe calm down a little bit so you can respond to their behavior from the best place in you — from your best intentions as a parent.
I can imagine skeptics out there, who say, “But kids need to figure out how to live in a world that really doesn’t care what they want. You’re pampering them!” In fact, you admit your own mother sometimes feels this way. What do you say to that?
I would never tell someone who’s using a discipline strategy that they feel really works that they’re wrong. What I say to my mom is, “The tools and strategies that you used and our grandparents used weren’t wrong, they just don’t work with modern kids.” Ultimately, we want to instill self-discipline in our children, which will never happen if we’re always controlling them.
If we respond to our kids’ misbehavior instead of reacting, we’ll get the results we want. I want to take a little of the pressure off of parenting; each instance is not life or death. We can let our kids struggle a little bit. We can let them fail. In fact, that is the process of childhood when children misbehave. It’s not a sign of our failure as parents. It’s normal.
For six years now, life has been really good for James. He has a great job as the creative director of an advertising firm in New York City. He enjoys spending time with his wife and kids.
And it has all been possible, he says, because for the past six years he has been taking a drug called ketamine.
Before ketamine, James was unable to work or focus his thoughts. His mind was filled with violent images. And his mood could go from ebullient to dark in a matter of minutes.
Ketamine “helped me get my life back,” says James, who asked that we not use his last name to protect his career.
Ketamine was developed as a human and animal anesthetic in the 1960s. And almost from the time it reached the market it has also been used as a mind-bending party drug.
But ketamine’s story took a surprising turn in 2006, when researchers at the National Institutes of Health showed that an intravenous dose could relieve severe depression in a matter of hours. Since then, doctors have prescribed ketamine “off label” to thousands of depressed patients who don’t respond to other drugs.
And pharmaceutical companies are testing several new ketamine-related drugs to treat depression. Johnson & Johnson expects to seek approval for its nasal spray esketamine later this year, though the approval would be limited to use in a clinical setting.
Meanwhile, doctors have begun trying ketamine on patients with a wide range of psychiatric disorders other than depression. And there is now growing evidence it can help people with anxiety, bipolar disorder, post-traumatic stress disorder, and perhaps even obsessive-compulsive disorder.
“I think it’s actually one of the biggest advances in psychiatry in a very long time,” says Dr. Martin Teicher, an associate professor of psychiatry at Harvard Medical School and director of the Developmental Biopsychiatry Research Program at McLean Hospital.
Ketamine may also offer new hope for people like James who have symptoms of several different psychiatric disorders.
James had a happy childhood, he says. But his thoughts were out of control. “I always felt like I was crossing a freeway and my thoughts were just racing past me,” he says.
He spent much of his childhood terrified of “an unknown, an ambiguous force out there.” The fear was “overwhelming,” he says. “I literally slept with the cover over my head with just room to breathe through my mouth until I went to college.”
And there was something else about James: his body temperature.
“I overheated constantly,” he says. “I would wear shorts all year long. In my 20s in my apartment I would sleep with the windows open in the middle of the winter.”
In his late 20s, James saw a doctor who told him he had attention deficit hyperactivity disorder. So he started taking stimulants.
At first, the pills helped him focus. Then they didn’t, no matter how many he took.
He’d done well as an idea guy in the advertising industry. But now James was trying to work at home, and it wasn’t going well.
“ADHD pills will make you interested in anything,” he says. “So I was putting the desk together and taking the desk apart. I was putting a laptop stand together and taking it apart. I was going in a massive downward spiral.”
James had always suffered from mood swings. But now they were rapid and extreme. And he couldn’t stop thinking about gruesome scenarios, like a murderer coming for his family.
“My wife took a summer off to be with me because she was scared of what was going to happen to me,” he says. “She would go to work for a few hours, then rush home. There would be times I’d call her just screaming, ‘Please come home. I can’t get through another minute.’ ”
Eventually, James found his way to Dr. Demitri Papolos, an associate professor of clinical psychiatry at Albert Einstein College of Medicine.
“He was like a whirling dervish when he came into my office,” Papolos says. “He was extremely fearful and scanning the environment all the time and he overheated at the drop of a hat.”
Papolos diagnosed James with a variant of bipolar disorder he calls the “fear of harm phenotype.” It typically appears in childhood and often doesn’t respond to traditional psychiatric drugs.
But Papolos has found that the condition does respond to ketamine. “It’s been transformational,” he says.
In January, Papolos published a study of 45 children with the problem. They inhaled a nasal mist containing ketamine about twice a week. Nearly all got dramatically better.
Scientists still aren’t sure why ketamine works, but there’s evidence that it encourages the brain to rewire, to alter the connections between cells. That process has been linked to recovery from depression. And it may also explain why ketamine helps people who have symptoms associated with several different psychiatric disorders.
“I think it’s having multiple effects, and that means it’s probably useful for multiple different disorders,” Teicher says.
One of those effects involves a part of the brain involved in temperature regulation. And that could explain why patients like James usually stop overheating once they are taking ketamine.
James started taking a ketamine nasal spray every other day. He says his response was dramatic.
“One day I turn to my wife and I’m like, ‘I feel calm today. I don’t know if it’s the sun coming in, I don’t know if it’s just the way we’re sitting here, but I feel like I could go and sit at the computer and work.’ ”
The next day, James did sit down at his computer. A month later, he was back at work.
“I’m one of the lucky ones,” says Judy Perkins, of the immunotherapy treatment she got. The experimental approach seems to have eradicated her metastatic breast cancer.”
Courtesy of Judy Perkins
Doctors at the National Institutes of Health say they’ve apparently completely eradicated cancer from a patient who had untreatable, advanced breast cancer.
The case is raising hopes about a new way to harness the immune system to fight some of the most common cancers. The methods and the patient’s experience are described Monday in a paper published in the journal Nature Medicine.
“We’re looking for a treatment — an immunotherapy — that can be broadly used in patients with common cancers,” says Dr. Steven Rosenberg, an oncologist and immunologist at the National Cancer Institute, who has been developing the approach.
Rosenberg’s team painstakingly analyzes the DNA in a sample of each patient’s cancer for mutations specific to their malignancies. Next, scientists sift through tumor tissue for immune system cells known as T cells that appear programmed to home in on those mutations.
But Rosenberg and others caution that the approach doesn’t work for everyone. In fact, it failed for two other breast cancer patients. Many more patients will have to be treated — and followed for much longer — to fully evaluate the treatment’s effectiveness, the scientists say.
Still, the treatment has helped seven of 45 patients with a variety of cancers, Rosenberg says. That’s a response rate of about 15 percent, and included patients with advanced cases of colon cancer, liver cancer and cervical cancer.
“Is it ready for prime time today? No,” Rosenberg says.”Can we do it in most patients today? No.”
But the treatment continues to be improved. “I think it’s the most promising treatment now being explored for solving the problem of the treatment of metastatic, common cancers,” he says.
The breast cancer patient helped by the treatment says it transformed her life.
“It’s amazing,” says Judy Perkins, 52, a retired engineer who lives in Port St. Lucie, Fla.
When Perkins was first diagnosed and treated for breast cancer in 2003, she thought she’d beaten the disease. “I thought I was done with it,” she says.
But about a decade later, she felt a new lump. Doctors discovered the cancer had already spread throughout her chest. Her prognosis was grim.
“I became a metastatic cancer patient,” says Perkins. “That was hard.”
Perkins went through round after round of chemotherapy. She tried every experimental treatment she could find. But the cancer kept spreading. Some of her tumors grew to the size of tennis balls.
Perkins received tumor infiltrating lymphocytes as treatment in 2015.
Courtesy of Stephanie Goff/NIH
“I had sort of essentially run out of arrows in my quiver,” she says. “While I would say I had some hope, I was also kind of like ready to quit, too.”
Then she heard about the experimental treatment at the NIH. It was designed to fight some of the most common cancers, including breast cancer.
“The excitement here is that we’re attacking the very mutations that are unique to that cancer — in that patient’s cancer and not in anybody else’s cancer. So it’s about as personalized a treatment as you can imagine,” Rosenberg says.
His team identified and then grew billions of T cells for Perkins in the lab and then infused them back into her body. They also gave her two drugs to help the cells do their job.
The treatment was grueling. Perkins says the hardest part was the side effects of a drug known as interleukin, which she received to help boost the effectiveness of the immune system cells. Interleukin causes severe flu-like symptoms, such as a high fever, intense malaise and uncontrollable shivering.
But the treatment apparently worked, Rosenberg reports. Perkins’ tumors soon disappeared. And, more than two years later, she remains cancer-free.
“All of her detectable disease has disappeared. It’s remarkable,” Rosenberg says.
Perkins is thrilled.
“I’m one of the lucky ones,” Perkins says. “We got the right T cells in the right place at the right time. And they went in and ate up all my cancer. And I’m cured. It’s freaking unreal.”
In an article accompanying the new paper, Laszlo Radvanyi, president and scientific director of the Ontario Institute for Cancer Research, calls the results “remarkable.”
The approach and other recent advances suggest scientists may be “at the cusp of a major revolution in finally realizing the elusive goal of being able to target the plethora of mutations in cancer through immunotherapy,” Radvanyi writes.
Other cancer researchers agree.
“When I saw this paper I thought: “Whoa! I mean, it’s very impressive,” says James Heath, president of the Institute for Systems Biology in Seattle.
“One of the most exciting breakthroughs in biomedicine over the past decade has been activating the immune system against various cancers. But they have not been successful in breast cancer. Metastatic breast cancer is basically a death sentence,” Heath says. “And this shows that you can reverse it. It’s a big deal.”
One key challenge will be to make the treatment easier, faster, and affordable, Rosenberg says. “We’re working literally around the clock to try to improve the treatment.”
An executive guide to artificial intelligence, from machine learning and general AI to neural networks.
What is artificial intelligence (AI)?
It depends who you ask.
AI might be a hot topic but you’ll still need to justify those projects.
Back in the 1950s, the fathers of the field Minsky and McCarthy, described artificial intelligence as any task performed by a program or a machine that, if a human carried out the same activity, we would say the human had to apply intelligence to accomplish the task.
That obviously is a fairly broad definition, which is why you will sometimes see arguments over whether something is truly AI or not.
AI systems will typically demonstrate at least some of the following behaviors associated with human intelligence: planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity.
At a very high level artificial intelligence can be split into two broad types: narrow AI and general AI.
Narrow AI is what we see all around us in computers today: intelligent systems that have been taught or learned how to carry out specific tasks without being explicitly programmed how to do so.
This type of machine intelligence is evident in the speech and language recognition of the Siri virtual assistant on the Apple iPhone, in the vision-recognition systems on self-driving cars, in the recommendation engines that suggest products you might like based on what you bought in the past. Unlike humans, these systems can only learn or be taught how to do specific tasks, which is why they are called narrow AI.
What can narrow AI do?
There are a vast number of emerging applications for narrow AI: interpreting video feeds from drones carrying out visual inspections of infrastructure such as oil pipelines, organizing personal and business calendars, responding to simple customer-service queries, co-ordinating with other intelligent systems to carry out tasks like booking a hotel at a suitable time and location, helping radiologists to spot potential tumors in X-rays, flagging inappropriate content online, detecting wear and tear in elevators from data gathered by IoT devices, the list goes on and on.
What can general AI do?
Artificial general intelligence is very different, and is the type of adaptable intellect found in humans, a flexible form of intelligence capable of learning how to carry out vastly different tasks, anything from haircutting to building spreadsheets, or to reason about a wide variety of topics based on its accumulated experience. This is the sort of AI more commonly seen in movies, the likes of HAL in 2001 or Skynet in The Terminator, but which doesn’t exist today and AI experts are fiercely divided over how soon it will become a reality.
A survey conducted among four groups of experts in 2012/13 by AI researchers Vincent C Müller and philosopher Nick Bostrom reported a 50 percent chance that Artificial General Intelligence (AGI) would be developed between 2040 and 2050, rising to 90 percent by 2075. The group went even further, predicting that so-called ‘ superintelligence‘ — which Bostrom defines as “any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest” — was expected some 30 years after the achievement of AGI.
That said, some AI experts believe such projections are wildly optimistic given our limited understanding of the human brain, and believe that AGI is still centuries away.
What is machine learning?
There is a broad body of research in AI, much of which feeds into and complements each other.
Currently enjoying something of a resurgence, machine learning is where a computer system is fed large amounts of data, which it then uses to learn how to carry out a specific task, such as understanding speech or captioning a photograph.
What are neural networks?
Key to the process of machine learning are neural networks. These are brain-inspired networks of interconnected layers of algorithms, called neurons, that feed data into each other, and which can be trained to carry out specific tasks by modifying the importance attributed to input data as it passes between the layers. During training of these neural networks, the weights attached to different inputs will continue to be varied until the output from the neural network is very close to what is desired, at which point the network will have ‘learned’ how to carry out a particular task.
A subset of machine learning is deep learning, where neural networks are expanded into sprawling networks with a huge number of layers that are trained using massive amounts of data. It is these deep neural networks that have fueled the current leap forward in the ability of computers to carry out task like speech recognition and computer vision.
There are various types of neural networks, with different strengths and weaknesses. Recurrent neural networks are a type of neural net particularly well suited to language processing and speech recognition, while convolutional neural networks are more commonly used in image recognition. The design of neural networks is also evolving, with researchers recently refining a more effective form of deep neural network called long short-term memory or LSTM, allowing it to operate fast enough to be used in on-demand systems like Google Translate.
The structure and training of deep neural networks.
Image: Nuance
Another area of AI research is evolutionary computation, which borrows from Darwin’s famous theory of natural selection, and sees genetic algorithms undergo random mutations and combinations between generations in an attempt to evolve the optimal solution to a given problem.
This approach has even been used to help design AI models, effectively using AI to help build AI. This use of evolutionary algorithms to optimize neural networks is called neuroevolution, and could have an important role to play in helping design efficient AI as the use of intelligent systems becomes more prevalent, particularly as demand for data scientists often outstrips supply. The technique was recently showcased by Uber AI Labs, which released papers on using genetic algorithms to train deep neural networks for reinforcement learning problems.
Finally there are expert systems, where computers are programmed with rules that allow them to take a series of decisions based on a large number of inputs, allowing that machine to mimic the behavior of a human expert in a specific domain. An example of these knowledge-based systems might be, for example, an autopilot system flying a plane.
What is fueling the resurgence in AI?
The biggest breakthroughs for AI research in recent years have been in the field of machine learning, in particular within the field of deep learning.
This has been driven in part by the easy availability of data, but even more so by an explosion in parallel computing power in recent years, during which time the use of GPU clusters to train machine-learning systems has become more prevalent.
Not only do these clusters offer vastly more powerful systems for training machine-learning models, but they are now widely available as cloud services over the internet. Over time the major tech firms, the likes of Google and Microsoft, have moved to using specialized chips tailored to both running, and more recently training, machine-learning models.
An example of one of these custom chips is Google’s Tensor Processing Unit (TPU), the latest version of which accelerates the rate at which useful machine-learning models built using Google’s TensorFlow software library can infer information from data, as well as the rate at which they can be trained.
As mentioned, machine learning is a subset of AI and is generally split into two main categories: supervised and unsupervised learning.
Supervised learning
A common technique for teaching AI systems is by training them using a very large number of labeled examples. These machine-learning systems are fed huge amounts of data, which has been annotated to highlight the features of interest. These might be photos labeled to indicate whether they contain a dog or written sentences that have footnotes to indicate whether the word ‘bass’ relates to music or a fish. Once trained, the system can then apply these labels can to new data, for example to a dog in a photo that’s just been uploaded.
Training these systems typically requires vast amounts of data, with some systems needing to scour millions of examples to learn how to carry out a task effectively — although this is increasingly possible in an age of big data and widespread data mining. Training datasets are huge and growing in size — Google’s Open Images Dataset has about nine million images, while its labeled video repository YouTube-8M links to seven million labeled videos. ImageNet, one of the early databases of this kind, has more than 14 million categorized images. Compiled over two years, it was put together by nearly 50,000 people — most of whom were recruited through Amazon Mechanical Turk — who checked, sorted, and labeled almost one billion candidate pictures.
In the long run, having access to huge labelled datasets may also prove less important than access to large amounts of compute power.
In recent years, Generative Adversarial Networks ( GANs) have shown how machine-learning systems that are fed a small amount of labelled data can then generate huge amounts of fresh data to teach themselves.
This approach could lead to the rise of semi-supervised learning, where systems can learn how to carry out tasks using a far smaller amount of labelled data than is necessary for training systems using supervised learning today.
Unsupervised learning
In contrast, unsupervised learning uses a different approach, where algorithms try to identify patterns in data, looking for similarities that can be used to categorise that data.
An example might be clustering together fruits that weigh a similar amount or cars with a similar engine size.
The algorithm isn’t setup in advance to pick out specific types of data, it simply looks for data that can be grouped by its similarities, for example Google News grouping together stories on similar topics each day.
Reinforcement learning
A crude analogy for reinforcement learning is rewarding a pet with a treat when it performs a trick.
In reinforcement learning, the system attempts to maximize a reward based on its input data, basically going through a process of trial and error until it arrives at the best possible outcome.
By also looking at the score achieved in each game the system builds a model of which action will maximize the score in different circumstances, for instance, in the case of the video game Breakout, where the paddle should be moved to in order to intercept the ball.
Many AI-related technologies are approaching, or have already reached, the ‘peak of inflated expectations’ in Gartner’s Hype Cycle, with the backlash-driven ‘trough of disillusionment’ lying in wait.
The Google subsidiary has struck a series of deals with organisations in the UK health service — so what’s really happening?
With AI playing an increasingly major role in modern software and services, each of the major tech firms is battling to develop robust machine-learning technology for use in-house and to sell to the public via cloud services.
Each regularly makes headlines for breaking new ground in AI research, although it is probably Google with its DeepMind AI AlphaGo that has probably made the biggest impact on the public awareness of AI.
Which AI services are available?
All of the major cloud platforms — Amazon Web Services, Microsoft Azure and Google Cloud Platform — provide access to GPU arrays for training and running machine learning models, with Google also gearing up to let users use its Tensor Processing Units — custom chips whose design is optimized for training and running machine-learning models.
All of the necessary associated infrastructure and services are available from the big three, the cloud-based data stores, capable of holding the vast amount of data needed to train machine-learning models, services to transform data to prepare it for analysis, visualisation tools to display the results clearly, and software that simplifies the building of models.
For those firms that don’t want to build their own machine learning models but instead want to consume AI-powered, on-demand services — such as voice, vision, and language recognition — Microsoft Azure stands out for the breadth of services on offer, closely followed by Google Cloud Platform and then AWS. Meanwhile IBM, alongside its more general on-demand offerings, is also attempting to sell sector-specific AI services aimed at everything from healthcare to retail, grouping these offerings together under its IBM Watson umbrella — and recently investing $2bn in buying The Weather Channel to unlock a trove of data to augment its AI services.
Which of the major tech firms is winning the AI race?
Internally, each of the tech giants — and others such as Facebook — use AI to help drive myriad public services: serving search results, offering recommendations, recognizing people and things in photos, on-demand translation, spotting spam — the list is extensive.
But one of the most visible manifestations of this AI war has been the rise of virtual assistants, such as Apple’s Siri, Amazon’s Alexa, the Google Assistant, and Microsoft Cortana.
Relying heavily on voice recognition and natural-language processing, as well as needing an immense corpus to draw upon to answer queries, a huge amount of tech goes into developing these assistants.
But while Apple’s Siri may have come to prominence first, it is Google and Amazon whose assistants have since overtaken Apple in the AI space — Google Assistant with its ability to answer a wide range of queries and Amazon’s Alexa with the massive number of ‘Skills’ that third-party devs have created to add to its capabilities.
It’d be a big mistake to think the US tech giants have the field of AI sewn up. Chinese firms Alibaba, Baidu, and Lenovo are investing heavily in AI in fields ranging from ecommerce to autonomous driving. As a country China is pursuing a three-step plan to turn AI into a core industry for the country, one that will be worth 150 billion yuan ($22bn) by 2020.
The combination of weak privacy laws, huge investment, concerted data-gathering, and big data analytics by major firms like Baidu, Alibaba, and Tencent, means that some analysts believe China will have an advantage over the US when it comes to future AI research, with one analyst describing the chances of China taking the lead over the US as 500 to one in China’s favor.
How can I get started with AI?
While you could try to build your own GPU array at home and start training a machine-learning model, probably the easiest way to experiment with AI-related services is via the cloud.
All of the major tech firms offer various AI services, from the infrastructure to build and train your own machine-learning models through to web services that allow you to access AI-powered tools such as speech, language, vision and sentiment recognition on demand.
What are recent landmarks in the development of AI?
There’s too many to put together a comprehensive list, but some recent highlights include: in 2009 Google showed it was possible for its self-driving Toyota Prius to complete more than 10 journeys of 100 miles each — setting society on a path towards driverless vehicles.
In 2011, the computer system IBM Watson made headlines worldwide when it won the US quiz show Jeopardy!, beating two of the best players the show had ever produced. To win the show, Watson used natural language processing and analytics on vast repositories of data that it processed to answer human-posed questions, often in a fraction of a second.
Since Watson’s win, perhaps the most famous demonstration of the efficacy of machine-learning systems was the 2016 triumph of the Google DeepMind AlphaGo AI over a human grandmaster in Go, an ancient Chinese game whose complexity stumped computers for decades. Go has about 200 moves per turn, compared to about 20 in Chess. Over the course of a game of Go, there are so many possible moves that searching through each of them in advance to identify the best play is too costly from a computational point of view. Instead, AlphaGo was trained how to play the game by taking moves played by human experts in 30 million Go games and feeding them into deep-learning neural networks.
Training these deep learning networks can take a very long time, requiring vast amounts of data to be ingested and iterated over as the system gradually refines its model in order to achieve the best outcome.
However, more recently Google refined the training process with AlphaGo Zero, a system that played “completely random” games against itself, and then learnt from the results. At last year’s prestigious Neural Information Processing Systems (NIPS) conference, Google DeepMind CEO Demis Hassabis revealed AlphaGo had also mastered the games of chess and shogi.
That same year, OpenAI created AI agents that invented their own invented their own language to cooperate and achieve their goal more effectively, shortly followed by Facebook training agents to negotiate and even lie.
How will AI change the world?
Robots and driverless cars
This ebook, based on a special feature from ZDNet and TechRepublic, looks at emerging autonomous transport technologies and how they will affect society and the future of business.
Machine-learning systems have helped computers recognize what people are saying with an accuracy of almost 95 percent. Recently Microsoft’s Artificial Intelligence and Research group reported it had developed a system able to transcribe spoken English as accurately as human transcribers.
With researchers pursuing a goal of 99 percent accuracy, expect speaking to computers to become the norm alongside more traditional forms of human-machine interaction.
Although privacy regulations vary across the world, it’s likely this more intrusive use of AI technology — including AI that can recognize emotions — will gradually become more widespread elsewhere.
Healthcare
AI could eventually have a dramatic impact on healthcare, helping radiologists to pick out tumors in x-rays, aiding researchers in spotting genetic sequences related to diseases and identifying molecules that could lead to more effective drugs.
There have been trials of AI-related technology in hospitals across the world. These include IBM’s Watson clinical decision support tool, which is trained by oncologists at Memorial Sloan Kettering Cancer Center, and the use of Google DeepMind systems by the UK’s National Health Service, where it will help spot eye abnormalities and streamline the process of screening patients for head and neck cancers.
Will AI kill us all?
Again, it depends who you ask. As AI-powered systems have grown more capable, so warnings of the downsides have become more dire.
Tesla and SpaceX CEO Elon Musk has claimed that AI is a “fundamental risk to the existence of human civilization”. As part of his push for stronger regulatory oversight and more responsible research into mitigating the downsides of AI he set up OpenAI, a non-profit artificial intelligence research company that aims to promote and develop friendly AI that will benefit society as a whole. Similarly, the esteemed physicist Stephen Hawking has warned that once a sufficiently advanced AI is created it will rapidly advance to the point at which it vastly outstrips human capabilities, a phenomenon known as the singularity, and could pose an existential threat to the human race.
Yet the notion that humanity is on the verge of an AI explosion that will dwarf our intellect seems ludicrous to some AI researchers.
While AI won’t replace all jobs, what seems to be certain is that AI will change the nature of work, with the only question being how rapidly and how profoundly automation will alter the workplace.
There is barely a field of human endeavour that AI doesn’t have the potential to impact. As AI expert Andrew Ng puts it: “many people are doing routine, repetitive jobs. Unfortunately, technology is especially good at automating routine, repetitive work”, saying he sees a “significant risk of technological unemployment over the next few decades”.
The evidence of which jobs will be supplanted is starting to emerge. Amazon has just launched Amazon Go, a cashier-free supermarket in Seattle where customers just take items from the shelves and walk out. What this means for the more than three million people in the US who works as cashiers remains to be seen. Amazon again is leading the way in using robots to improve efficiency inside its warehouses. These robots carry shelves of products to human pickers who select items to be sent out. Amazon has more than 100,000 bots in its fulfilment centers, with plans to add many more. But Amazon also stresses that as the number of bots have grown, so has the number of human workers in these warehouses. However, Amazon and small robotics firms are working to automate the remaining manual jobs in the warehouse, so it’s not a given that manual and robotic labor will continue to grow hand-in-hand.
Fully autonomous self-driving vehicles aren’t a reality yet, but by some predictions the self-driving trucking industry alone is poised to take over 1.7 million jobs in the next decade, even without considering the impact on couriers and taxi drivers.
Yet some of the easiest jobs to automate won’t even require robotics. At present there are millions of people working in administration, entering and copying data between systems, chasing and booking appointments for companies. As software gets better at automatically updating systems and flagging the information that’s important, so the need for administrators will fall.
As with every technological shift, new jobs will be created to replace those lost. However, what’s uncertain is whether these new roles will be created rapidly enough to offer employment to those displaced, and whether the newly unemployed will have the necessary skills or temperament to fill these emerging roles.
Not everyone is a pessimist. For some, AI is a technology that will augment, rather than replace, workers. Not only that but they argue there will be a commercial imperative to not replace people outright, as an AI-assisted worker — think a human concierge with an AR headset that tells them exactly what a client wants before they ask for it — will be more productive or effective than an AI working on its own.
Among AI experts there’s a broad range of opinion about how quickly artificially intelligent systems will surpass human capabilities.
Notable dates included AI writing essays that could pass for being written by a human by 2026, truck drivers being made redundant by 2027, AI surpassing human capabilities in retail by 2031, writing a best-seller by 2049, and doing a surgeon’s work by 2053.
They estimated there was a relatively high chance that AI beats humans at all tasks within 45 years and automates all human jobs within 120 years.
Not really, but we do need think carefully about how to harness, and regulate, machine intelligence.
By now, most of us are used to the idea of rapid, even accelerating, technological change, particularly where information technologies are concerned. Indeed, as consumers, we helped the process along considerably. We love the convenience of mobile phones, and the lure of social-media platforms such as Facebook, even if, as we access these services, we find that bits and pieces of our digital selves become strewn all over the internet.
More and more tasks are being automated. Computers (under human supervision) already fly planes and sail ships. They are rapidly learning how to drive cars. Automated factories make many of our consumer goods. If you enter (or return to) Australia with an eligible e-passport, a computer will scan your face, compare it with your passport photo and, if the two match up, let you in. The “internet of things” beckons; there seems to be an “app” for everything. We are invited to make our homes smarter and our lives more convenient by using programs that interface with our home-based systems and appliances to switch the lights on and off, defrost the fridge and vacuum the carpet.
With the demise of the local car industry and the decline of manufacturing, the services sector is expected to pick up the slack for job seekers. But robots are taking over certain roles once deemed human-only.
Clever though they are, these programs represent more-or-less familiar applications of computer-based processing power. With artificial intelligence, though, computers are poised to conquer skills that we like to think of as uniquely human: the ability to extract patterns and solve problems by analysing data, to plan and undertake tasks, to learn from our own experience and that of others, and to deploy complex forms of reasoning.
The quest for AI has engaged computer scientists for decades. Until very recently, though, AI’s initial promise had failed to materialise. The recent revival of the field came as a result of breakthrough advances in machine intelligence and, specifically, machine learning. It was found that, by using neural networks (interlinked processing points) to implement mathematically specified procedures or algorithms, machines could, through many iterations, progressively improve on their performance – in other words, they could learn. Machine intelligence in general and machine learning in particular are now the fastest-growing components of AI.
The achievements have been impressive. It is now 20 years since IBM’s Deep Blue program, using traditional computational approaches, beat Garry Kasparov, the world’s best chess player. With machine-learning techniques, computers have conquered even more complex games such as Go, a strategy-based game with an enormous range of possible moves. In 2016, Google’s Alpha Go program beat Lee Sedol, the world’s best Go player, in a four-game match.
Allan Dafoe, of Oxford University’s future humanities institute, says AI is already at the point where it can transform almost every industry, from agriculture to health and medicine, from energy systems to security and the military. With sufficient data, computing power and an appropriate algorithm, machines can be used to come up with solutions that are not only commercially useful but, in some cases, novel and even innovative.
Should we be worried? Commentators as diverse as the late Stephen Hawking and development economist Muhammad Yunus have issued dire warnings about machine intelligence. Unless we learn how to control AI, they argue, we risk finding ourselves replaced by machines far more intelligent than we are. The fear is that not only will humans be redundant in this brave new world, but the machines will find us completely useless and eliminate us.
The University of Canberra’s robot Ardie teaches tai chi to primary school pupils.
If these fears are realistic, then governments clearly need to impose some sort of ethical and values-based framework around this work. But are our regulatory and governance techniques up to the task? When, in Australia, we have struggled to regulate our financial services industry, how on earth will governments anywhere manage a field as rapidly changing and complex as machine intelligence?
Governments often seem to play catch-up when it comes to new technologies. Privacy legislation is enormously difficult to enforce when technologies effortlessly span national boundaries. It is difficult for legislators even to know what is going on in relation to new applications developed inside large companies such as Facebook. On the other hand, governments are hardly IT ingenues. The public sector provided the demand-pull that underwrote the success of many high-tech firms. The US government, in particular, has facilitated the growth of many companies in cybersecurity and other fields.
Governments have been in the information business for a very long time. As William the Conqueror knew when he ordered his Domesday Book to be compiled in 1085, you can’t tax people successfully unless you know something about them. Spending of tax-generated funds is impossible without good IT. In Australia, governments have developed and successfully managed very large databases in health and human services.
The governance of all this data is subject to privacy considerations, sometimes even at the expense of information-sharing between agencies. The evidence we have is that, while some people worry a lot about privacy, most of us are prepared to trust government with our information. In 2016, the Australian Bureau of Statistics announced that, for the first time, it would retain the names and addresses it collected during the course of the 2016 population census. It was widely expected (at least by the media) that many citizens would withhold their names and addresses when they returned their forms. In the end, very few did.
But these are government agencies operating outside the security field. The so-called “deep state” holds information about citizens that could readily be misused. Moreover, private-sector profit is driving much of the current AI surge (although, in many cases, it is the thrill of new knowledge and understanding, too). We must assume that criminals are working out ways to exploit these possibilities, too.
If we want values such as equity, transparency, privacy and safety to govern what happens, old-fashioned regulation will not do the job. We need the developers of these technologies to co-produce the values we require, which implies some sort of effective partnership between the state and the private sector.
Could policy development be the basis for this kind of partnership? At the moment, machine intelligence works best on problems for which relevant data is available, and the objective is relatively easy to specify. As it develops, and particularly if governments are prepared to share their own data sets, machine intelligence could become important in addressing problems such as climate change, where we have data and an overall objective, but not much idea as to how to get there.
Machine intelligence might even help with problems where objectives are much harder to specify. What, for example, does good urban planning look like? We can crunch data from many different cities, and come up with an answer that could, in theory, go well beyond even the most advanced human-based modelling. When we don’t know what we don’t know, machines could be very useful indeed. Nor do we know, until we try, how useful the vast troves of information held by governments might be.
Perhaps, too, the jobs threat is not as extreme as we fear. Experience shows that humans are very good at finding things to do. And there might not be as many existing jobs at risk as we suppose. I am convinced, for example, that no robot could ever replace road workers – just think of the fantastical patterns of dug-up gravel and dirt they produce, the machines artfully arranged by the roadside or being driven, very slowly, up and down, even when all the signs are there, and there is absolutely no one around. How do we get a robot, even one capable of learning by itself, to do all that?
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