Tag Archives: answer
People are often quick to link artificial intelligence with the future of every industry including technology, medicine, and science. For most scientists, there is a common belief that the answer lies in data mining through the information we have already generated online. Whereas humans cannot analyze large amounts of data, AI can produce fast, accurate …
The post How AI Can Overcome Planet’s Challenges: 5 Ways How Artificial Intelligence May Help to Save the Planet appeared first on TFOT. Continue reading
When? This is probably the question that futurists, AI experts, and even people with a keen interest in technology dread the most. It has proved famously difficult to predict when new developments in AI will take place. The scientists at the Dartmouth Summer Research Project on Artificial Intelligence in 1956 thought that perhaps two months would be enough to make “significant advances” in a whole range of complex problems, including computers that can understand language, improve themselves, and even understand abstract concepts.
Sixty years later, and these problems are not yet solved. The AI Index, from Stanford, is an attempt to measure how much progress has been made in artificial intelligence.
The index adopts a unique approach, and tries to aggregate data across many regimes. It contains Volume of Activity metrics, which measure things like venture capital investment, attendance at academic conferences, published papers, and so on. The results are what you might expect: tenfold increases in academic activity since 1996, an explosive growth in startups focused around AI, and corresponding venture capital investment. The issue with this metric is that it measures AI hype as much as AI progress. The two might be correlated, but then again, they may not.
The index also scrapes data from the popular coding website Github, which hosts more source code than anyone in the world. They can track the amount of AI-related software people are creating, as well as the interest levels in popular machine learning packages like Tensorflow and Keras. The index also keeps track of the sentiment of news articles that mention AI: surprisingly, given concerns about the apocalypse and an employment crisis, those considered “positive” outweigh the “negative” by three to one.
But again, this could all just be a measure of AI enthusiasm in general.
No one would dispute the fact that we’re in an age of considerable AI hype, but the progress of AI is littered by booms and busts in hype, growth spurts that alternate with AI winters. So the AI Index attempts to track the progress of algorithms against a series of tasks. How well does computer vision perform at the Large Scale Visual Recognition challenge? (Superhuman at annotating images since 2015, but they still can’t answer questions about images very well, combining natural language processing and image recognition). Speech recognition on phone calls is almost at parity.
In other narrow fields, AIs are still catching up to humans. Translation might be good enough that you can usually get the gist of what’s being said, but still scores poorly on the BLEU metric for translation accuracy. The AI index even keeps track of how well the programs can do on the SAT test, so if you took it, you can compare your score to an AI’s.
Measuring the performance of state-of-the-art AI systems on narrow tasks is useful and fairly easy to do. You can define a metric that’s simple to calculate, or devise a competition with a scoring system, and compare new software with old in a standardized way. Academics can always debate about the best method of assessing translation or natural language understanding. The Loebner prize, a simplified question-and-answer Turing Test, recently adopted Winograd Schema type questions, which rely on contextual understanding. AI has more difficulty with these.
Where the assessment really becomes difficult, though, is in trying to map these narrow-task performances onto general intelligence. This is hard because of a lack of understanding of our own intelligence. Computers are superhuman at chess, and now even a more complex game like Go. The braver predictors who came up with timelines thought AlphaGo’s success was faster than expected, but does this necessarily mean we’re closer to general intelligence than they thought?
Here is where it’s harder to track progress.
We can note the specialized performance of algorithms on tasks previously reserved for humans—for example, the index cites a Nature paper that shows AI can now predict skin cancer with more accuracy than dermatologists. We could even try to track one specific approach to general AI; for example, how many regions of the brain have been successfully simulated by a computer? Alternatively, we could simply keep track of the number of professions and professional tasks that can now be performed to an acceptable standard by AI.
“We are running a race, but we don’t know how to get to the endpoint, or how far we have to go.”
Progress in AI over the next few years is far more likely to resemble a gradual rising tide—as more and more tasks can be turned into algorithms and accomplished by software—rather than the tsunami of a sudden intelligence explosion or general intelligence breakthrough. Perhaps measuring the ability of an AI system to learn and adapt to the work routines of humans in office-based tasks could be possible.
The AI index doesn’t attempt to offer a timeline for general intelligence, as this is still too nebulous and confused a concept.
Michael Woodridge, head of Computer Science at the University of Oxford, notes, “The main reason general AI is not captured in the report is that neither I nor anyone else would know how to measure progress.” He is concerned about another AI winter, and overhyped “charlatans and snake-oil salesmen” exaggerating the progress that has been made.
A key concern that all the experts bring up is the ethics of artificial intelligence.
Of course, you don’t need general intelligence to have an impact on society; algorithms are already transforming our lives and the world around us. After all, why are Amazon, Google, and Facebook worth any money? The experts agree on the need for an index to measure the benefits of AI, the interactions between humans and AIs, and our ability to program values, ethics, and oversight into these systems.
Barbra Grosz of Harvard champions this view, saying, “It is important to take on the challenge of identifying success measures for AI systems by their impact on people’s lives.”
For those concerned about the AI employment apocalypse, tracking the use of AI in the fields considered most vulnerable (say, self-driving cars replacing taxi drivers) would be a good idea. Society’s flexibility for adapting to AI trends should be measured, too; are we providing people with enough educational opportunities to retrain? How about teaching them to work alongside the algorithms, treating them as tools rather than replacements? The experts also note that the data suffers from being US-centric.
We are running a race, but we don’t know how to get to the endpoint, or how far we have to go. We are judging by the scenery, and how far we’ve run already. For this reason, measuring progress is a daunting task that starts with defining progress. But the AI index, as an annual collection of relevant information, is a good start.
Image Credit: Photobank gallery / Shutterstock.com Continue reading
In an interview at Singularity University’s Exponential Medicine in San Diego, Richard Wender, chief cancer control officer at the American Cancer Society, discussed how technology has changed cancer care and treatment in recent years.
Just a few years ago, microscopes were the primary tool used in cancer diagnoses, but we’ve come a long way since.
“We still look at a microscope, we still look at what organ the cancer started in,” Wender said. “But increasingly we’re looking at the molecular signature. It’s not just the genomics, and it’s not just the genes. It’s also the cellular environment around that cancer. We’re now targeting our therapies to the mutations that are found in that particular cancer.”
Cancer treatments in the past have been largely reactionary, but they don’t need to be. Most cancer is genetic, which means that treatment can be preventative. This is one reason why newer cancer treatment techniques are searching for actionable targets in the specific gene before the cancer develops.
When asked how artificial intelligence and machine learning technologies are reshaping clinical trials, Wender acknowledged that how clinical trials have been run in the past won’t work moving forward.
“Our traditional ways of learning about cancer were by finding a particular cancer type and conducting a long clinical trial that took a number of years enrolling patients from around the country. That is not how we’re going to learn to treat individual patients in the future.”
Instead, Wender emphasized the need for gathering as much data as possible, and from as many individual patients as possible. This data should encompass clinical, pathological, and molecular data and should be gathered from a patient all the way through their final outcome. “Literally every person becomes a clinical trial of one,” Wender said.
For the best cancer treatment and diagnostics, Wender says the answer is to make the process collaborative by pulling in resources from organizations and companies that are both established and emerging.
It’s no surprise to hear that the best solutions come from pairing together uncommon partners to innovate.
Image Credit: jovan vitanovski / Shutterstock.com Continue reading
The tech industry touts its ability to automate tasks and remove slow and expensive humans from the equation. But in the background, a lot of the legwork training machine learning systems, solving problems software can’t, and cleaning up its mistakes is still done by people.
This was highlighted recently when Expensify, which promises to automatically scan photos of receipts to extract data for expense reports, was criticized for sending customers’ personally identifiable receipts to workers on Amazon’s Mechanical Turk (MTurk) crowdsourcing platform.
The company uses text analysis software to read the receipts, but if the automated system falls down then the images are passed to a human for review. While entrusting this job to random workers on MTurk was maybe not so wise—and the company quickly stopped after the furor—the incident brought to light that this kind of human safety net behind AI-powered services is actually very common.
As Wired notes, similar services like Ibotta and Receipt Hog that collect receipt information for marketing purposes also use crowdsourced workers. In a similar vein, while most users might assume their Facebook newsfeed is governed by faceless algorithms, the company has been ramping up the number of human moderators it employs to catch objectionable content that slips through the net, as has YouTube. Twitter also has thousands of human overseers.
Humans aren’t always witting contributors either. The old text-based reCAPTCHA problems Google used to use to distinguish humans from machines was actually simultaneously helping the company digitize books by getting humans to interpret hard-to-read text.
“Every product that uses AI also uses people,” Jeffrey Bigham, a crowdsourcing expert at Carnegie Mellon University, told Wired. “I wouldn’t even say it’s a backstop so much as a core part of the process.”
Some companies are not shy about their use of crowdsourced workers. Startup Eloquent Labs wants to insert them between customer service chatbots and human agents who step in when the machines fail. Many times the AI is pretty certain what particular work means, and an MTurk worker can step in and quickly classify them faster and cheaper than a service agent.
Fashion retailer Gilt provides “pre-emptive shipping,” which uses data analytics to predict what people will buy to get products to them faster. The company uses MTurk workers to provide subjective critiques of clothing that feed into their models.
MTurk isn’t the only player. Companies like Cloudfactory and Crowdflower provide crowdsourced human manpower tailored to particular niches, and some companies prefer to maintain their own communities of workers. Unlabel uses an army of 50,000 humans to check and edit the translations its artificial intelligence system produces for customers.
Most of the time these human workers aren’t just filling in the gaps, they’re also helping to train the machine learning component of these companies’ services by providing new examples of how to solve problems. Other times humans aren’t used “in-the-loop” with AI systems, but to prepare data sets they can learn from by labeling images, text, or audio.
It’s even possible to use crowdsourced workers to carry out tasks typically tackled by machine learning, such as large-scale image analysis and forecasting.
Zooniverse gets citizen scientists to classify images of distant galaxies or videos of animals to help academics analyze large data sets too complex for computers. Almanis creates forecasts on everything from economics to politics with impressive accuracy by giving those who sign up to the website incentives for backing the correct answer to a question. Researchers have used MTurkers to power a chatbot, and there’s even a toolkit for building algorithms to control this human intelligence called TurKit.
So what does this prominent role for humans in AI services mean? Firstly, it suggests that many tools people assume are powered by AI may in fact be relying on humans. This has obvious privacy implications, as the Expensify story highlighted, but should also raise concerns about whether customers are really getting what they pay for.
One example of this is IBM’s Watson for oncology, which is marketed as a data-driven AI system for providing cancer treatment recommendations. But an investigation by STAT highlighted that it’s actually largely driven by recommendations from a handful of (admittedly highly skilled) doctors at Memorial Sloan Kettering Cancer Center in New York.
Secondly, humans intervening in AI-run processes also suggests AI is still largely helpless without us, which is somewhat comforting to know among all the doomsday predictions of AI destroying jobs. At the same time, though, much of this crowdsourced work is monotonous, poorly paid, and isolating.
As machines trained by human workers get better at all kinds of tasks, this kind of piecemeal work filling in the increasingly small gaps in their capabilities may get more common. While tech companies often talk about AI augmenting human intelligence, for many it may actually end up being the other way around.
Image Credit: kentoh / Shutterstock.com Continue reading