From Apple to Google to Toyota, companies across the world are pouring resources into developing AI systems with machine learning. This comprehensive guide explains what machine learning really means.
Artificial intelligence (AI), which has been around since the 1950s, has seen ebbs and flows in popularity over the last 60+ years. But today, with the recent explosion of big data, high-powered parallel processing, and advanced neural algorithms, we are seeing a renaissance in AI–and companies from Amazon to Facebook to Google are scrambling to take the lead. According to AI expert Roman Yampolskiy, 2016 was the year of “AI on steroids,” and its explosive growth hasn’t stopped.
While there are different forms of AI, machine learning (ML) represents today’s most widely valued mechanism for reaching intelligence. Here’s what it means.
- What is machine learning? Machine learning is a subfield of artificial intelligence. Instead of relying on explicit programming, it is a system through which computers use a massive set of data and apply algorithms to “train” on–to teach themselves–and make predictions.
- When did machine learning become popular? The term “artificial intelligence” was coined in the 1950s by Alan Turing. Machine learning became popular in the 1990s, and returned to the public eye when Google’s DeepMind beat the world champion of Go in 2016. Since then, ML applications and machine learning’s popularity have only increased.
- Why does machine learning matter? Machine learning systems are able to quickly apply knowledge and training from large data sets to excel at facial recognition, speech recognition, object recognition, translation, and many other tasks.
- Which industries use machine learning? Machine learning touches industries spanning from government to education to healthcare. It can be used by businesses focused on marketing, social media, customer service, driverless cars, and many more. It is now widely regarded as a core tool for decision making.
- How do businesses use machine learning? Business applications of machine learning are numerous, but all boil down to one type of use: Processing, sorting, and finding patterns in huge amounts of data that would be impractical for humans to make sense of.
- What are the security and ethical concerns about machine learning? AI has already been trained to bypass advanced antimalware software, and it has the potential to be a huge security risk in the future. Ethical concerns also abound, especially in relation to the loss of jobs and the practicality of allowing machines to make moral decisions like those that would be necessary in self-driving vehicles.
- What machine learning tools are available? Businesses like IBM, Amazon, Microsoft, Google, and others offer tools for machine learning. There are free platforms as well.
SEE: Managing AI and ML in the enterprise 2020: Tech leaders increase project development and implementation (TechRepublic Premium)
What is machine learning?
Machine learning is a branch of AI. Other tools for reaching AI include rule-based engines, evolutionary algorithms, and Bayesian statistics. While many early AI programs, like IBM’s Deep Blue, which defeated Garry Kasparov in chess in 1997, were rule-based and dependent on human programming, machine learning is a tool through which computers have the ability to teach themselves, and set their own rules. In 2016, Google’s DeepMind beat the world champion in Go by using machine learning–training itself on a large data set of expert moves.
There are several kinds of machine learning:
- In supervised learning, the “trainer” will present the computer with certain rules that connect an input (an object’s feature, like “smooth,” for example) with an output (the object itself, like a marble).
- In unsupervised learning, the computer is given inputs and is left alone to discover patterns.
- In reinforcement learning, a computer system receives input continuously (in the case of a driverless car receiving input about the road, for example) and constantly is improving.
A massive amount of data is required to train algorithms for machine learning. First, the “training data” must be labeled (e.g., a GPS location attached to a photo). Then it is “classified.” This happens when features of the object in question are labeled and put into the system with a set of rules that lead to a prediction. For example, “red” and “round” are inputs into the system that leads to the output: Apple. Similarly, a learning algorithm could be left alone to create its own rules that will apply when it is provided with a large set of the object–like a group of apples, and the machine figures out that they have properties like “round” and “red” in common.
Many cases of machine learning involve “deep learning,” a subset of ML that uses algorithms that are layered, and form a network to process information and reach predictions. What distinguishes deep learning is the fact that the system can learn on its own, without human training.
When did machine learning become popular?
Machine learning was popular in the 1990s, and has seen a recent resurgence. Here are some timeline highlights.
- 2011: Google Brain was created, which was a deep neural network that could identify and categorize objects.
- 2014: Facebook’s DeepFace algorithm was introduced. The algorithm could recognize people from a set of photos.
- 2015: Amazon launched its machine learning platform, and Microsoft offered a Distributed Machine Learning Toolkit.
- 2016: Google’s DeepMind program “AlphaGo” beat the world champion, Lee Sedol, at the complex game of Go.
- 2017: Google announced that its machine learning tools can recognize objects in photos and understand speech better than humans.
- 2018: Alphabet subsidiary Waymo launched the ML-powered self-driving ride hailing service in Phoenix, AZ.
- 2020: Machine learning algorithms are brought into play against the COVID-19 pandemic, helping to speed vaccine research and improve the ability to track the virus’ spread.
Why does machine learning matter?
Aside from the tremendous power machine learning has to beat humans at games like Jeopardy, chess, and Go, machine learning has many practical applications. Machine learning tools are used to translate messages on Facebook, spot faces from photos, and find locations around the globe that have certain geographic features. IBM Watson is used to help doctors make cancer treatment decisions. Driverless cars use machine learning to gather information from the environment. Machine learning is also central to fraud prevention. Unsupervised machine learning, combined with human experts, has been proven to be very accurate in detecting cybersecurity threats, for example.
While there are many potential benefits of AI, there are also concerns about its usage. Many worry that AI (like automation) will put human jobs at risk. And whether or not AI replaces humans at work, it will definitely shift the kinds of jobs that are necessary. Machine learning’s requirement for labeled data, for example, has meant a huge need for humans to manually do the labeling.
As machine learning and AI in the workplace have evolved, many of its applications have centered on assisting workers rather than replacing them outright. This was especially true during the COVID-19 pandemic, which forced many companies to send large portions of their workforce home to work remotely, leading to AI bots and machine learning supplementing humans to take care of mundane tasks.
There are several institutions dedicated to exploring the impact of artificial intelligence. Here are a few (culled from our Twitter list of AI insiders).
- The Future of Life Institute brings together some of the greatest minds–from the co-founder of Skype to professors at Harvard and MIT–to explore some of the big questions about our future with machines. This Cambridge-based institute also has a stellar lineup on its scientific advisory board, from Nick Bostrom to Elon Musk to Morgan Freeman.
- The Future of Humanity Institute at Oxford is one of the premier sites for cutting-edge academic research. The FHI Twitter feed is a wonderful place for content on the latest in AI, and the many retweets by the account are also useful in finding other Twitter users who are working on the latest in artificial intelligence.
- The Machine Intelligence Research Institute at Berkeley is an excellent resource for the latest academic work in artificial intelligence. MIRI exists, according to Twitter, not only to investigate AI, but also to “ensure that the creation of smarter-than-human intelligence has a positive impact.”
Which industries use machine learning?
Just about any organization that wants to capitalize on its data to gain insights, improve relationships with customers, increase sales, or be competitive at a specific task will rely on machine learning. It has applications in government, business, education–virtually anyone who wants to make predictions, and has a large enough data set, can use machine learning to achieve their goals.
Along with analytics, machine learning can be used to supplement human workers by taking on mundane tasks and freeing them to do more meaningful, innovative, and productive work. Like with analytics, and business that has employees dealing with repetitive, high-volume tasks can benefit from machine learning.
How do businesses use machine learning?
2017 was a huge year for growth in the capabilities of machine learning, and 2018 set the stage for explosive growth that, by early 2020, found that 85% of businesses were using some form of AI in their deployed applications.
One of the things that may be holding that growth back, Deloitte said, is confusion–just what is machine learning capable of doing for businesses?
There are numerous examples of how businesses are leveraging machine learning, and all of it breaks down to the same basic thing: Processing massive amounts of data to draw conclusions much faster than a team of data scientists ever could.
Some examples of business uses of machine learning include:
- Alphabet-owned security firm Chronicle is using machine learning to identify cyberthreats and minimize the damage they can cause.
- Airbus Defense & Space is using ML-based image recognition technology to decrease the error rate of cloud recognition in satellite images.
- Global Fishing Watch is fighting overfishing by monitoring the GPS coordinates of fishing vessels, which has enabled them to monitor the whole ocean at once.
- Insurance firm AXA raised accident prediction accuracy by 78% by using machine learning to build accurate driver risk profiles.
- Japanese food safety company Kewpie has automated detection of defective potato cubes so that workers don’t have to spend hours watching for them.
- Yelp uses deep learning to classify photos people take of businesses by certain tags.
- MIT’s OptiVax can develop and test peptide vaccines for COVID-19 and other diseases in a completely virtual environment with variables including geographic coverage, population data, and more.
SEE: Executive’s guide to AI in business (free ebook) (TechRepublic)
Any business that deals with big data analysis can use machine learning technology to speed up the process and put humans to better use, and the particulars can vary greatly from industry to industry.
AI applications don’t come first–they’re tools used to solve business problems, and should be seen as such. Finding the proper application for machine learning technology involves asking the right questions, or being faced with a massive wall of data that would be impossible for a human to process.
What are the security and ethical concerns about machine learning?
There are a number of concerns about using machine learning and AI, including the security of cloud-hosted data and the ethical considerations of self-driving cars.
From a security perspective, there are always concerns about the theft of large amounts of data, but security fears go beyond how to lock down data repositories.
Security professionals are nearly universally concerned about the potential of AI to bypass antimalware software and other security measures, and they’re right to be worried: Artificial intelligence software has been developed that can modify malware to bypass AI-powered antimalware platforms.
Several tech leaders, like Elon Musk, Stephen Hawking, and Bill Gates, have expressed worries about how AI may be misused, and the importance of creating ethical AI. Evidenced by the disaster of Microsoft’s racist chatbot, Tay, AI can go wrong if left unmonitored.
Ethical concerns abound in the machine learning world as well; one example is a self-driving vehicle adaptation of the trolley problem thought experiment. In short, when a self-driving vehicle is presented with a choice between killing its occupants or a pedestrian, which is the right choice to make? There’s no clear answer with philosophical problems like this one–no matter how the machine is programmed, it has to make a moral judgement about the value of human lives.
Deep fake videos, which realistically replace one person’s face and/or voice with someone else’s based on photos and other recordings, have the potential to upset elections, insert unwilling people into pornography, and otherwise insert individuals into situtations they aren’t okay with. The far-reaching effects of this machine learning-powered tool could be devastating.
Along with whether giving learning machines the ability to make moral decisions is correct, or whether access to certain ML tools is socially dangerous, there are issues of the other major human cost likely to come with machine learning: Job loss.
If the AI revolution is truly the next major shift in the world, there are a lot of jobs that will cease to exist, and it isn’t necessarily the ones you’d think. While many low-skilled jobs are definitely at risk of being eliminated, so are jobs that require a high degree of training but are based on simple concepts like pattern recognition.
Radiologists, pathologists, oncologists, and other similar professions are all based on finding and diagnosing irregularities, something that machine learning is particularly suited to do.
There’s also the ethical concern of barrier to entry–while machine learning software itself isn’t expensive, only the largest enterprises in the world have the vast stores of data necessary to properly train learning machines to provide reliable results.
As time goes on, some experts predict that it’s going to become more difficult for smaller firms to make an impact, making machine learning primarily a game for the largest, wealthiest companies.
What machine learning tools are available?
There are many online resources about machine learning. To get an overview of how to create a machine learning system, check out this series of YouTube videos by Google Developer. There are also classes on machine learning from Coursera and many other institutions.
And to integrate machine learning into your organization, you can use resources like Microsoft’s Azure, Google Cloud Machine Learning, Amazon Machine Learning, IBM Watson, and free platforms like Scikit.
Editor’s note: This article was updated by Brandon Vigliarolo.
Signal Back Up: Users May See Some Errors, Company Says Will Be Fixed in Next Update
Signal said it had restored its services a day after the application faced technical difficulties as it dealt with a flood of new users after rival messaging app WhatsApp announced a controversial change in privacy terms.
Signal users might see errors in some chats as a side effect to the outage, but will be resolved in the next update of the app, the company said in a tweet.
As an unfortunate side effect of this outage, users might see errors in some of their chats. This does not affect your chat’s security, but you may have missed a message from that contact. The next Signal app updates will fix this automatically. Here’s what you can do now…
— Signal (@signalapp) January 17, 2021
The error does not affect the security of the chat, the company added.
The non-profit Signal Foundation based in Silicon Valley, which currently oversees the app, was launched in February 2018 with Brian Acton, who co-founded WhatsApp before selling it to Facebook, providing initial funding of $50 million (roughly Rs. 365 crores).
Signal faced a global outage that began on January 15. Although users could open the app and send messages, nothing was actually delivered.
Signal later sent Gadgets 360 a message with the following statement from its COO Aruna Harder: “We have been adding new servers and extra capacity at a record pace every single day this week, but today exceeded even our most optimistic projections. Millions upon millions of new users are sending a message that privacy matters, and we are working hard to restore service for them as quickly as possible.”
© Thomson Reuters 2021
CES 2021 wrap up: How enterprise tech makes all those smart toilets and robots possible
From smart toilets and disinfecting robots to transparent OLED displays and sleep tech, CES 2021 was a showcase for the latest innovations in consumer and enterprise technology.
CES 2021 is a wrap. And although this year’s all-digital event was a significantly different experience from past shows, there was plenty of innovative tech on display. TechRepublic’s Steve Ranger, Teena Maddox, and Bill Detwiler join Karen Roby to discuss the products and technology trends that stood out. The following is a transcript of their discuss edited for readability.
Smart toilets, disaffecting robots and a flying Cadillac
Karen Roby: Teena, let’s start with you, just general impressions from the show and some things that maybe stood out to you.
Teena Maddox: Sure. As always, it was an interesting CES, full of really cool products. Even though this one was virtual, we still managed to find some really great things to write about for TechRepublic. One of the things that really stood out for me was just the fact that there was so much creativity still going on and people were still really interested. You had your virtual groups of people surrounding products. One of the things that got a lot of attention online was the product from TOTO that… I know you did a video about that, the wellness toilet that, not to get gross here, but it lets you know how you’re doing based on your bodily functions. I thought that was really interesting. That got a lot of attention.
And then there was that really top of the line tub from Kohler that tops out around, I think, $16,000 that just gives you like this virtual environment. It has lighting, it has fog, it has music. It has a little bit of everything, and I really want that tub for my bathroom, but there’s no way I’m going to spend as much as a small car on a tub for my bathroom. So that got attention.
We wrote about tons of gaming monitors and laptops from so many fantastic brands, Dell and Acer and Lenovo, HP, everybody just really came out with some really great products. I talked to HyperX and they talked about how, they’re known for making gaming products, headsets and microphones and things like that, that gamers and streamers use, but everybody’s been buying them in this past year of course to work from home because they’re also great products to use as you’re doing things like we’re doing now, doing an online meeting, online videos. So they’ve really been working toward that and people have been using their products for double duty. So they introduced some new products and we wrote about those.
There’s just been so much cool stuff. There’s a lot of sleep tech, a lot of fitness tech and to be expected, there were a lot of masks that had really high tech features because tying tech with masks. I think some of them are a little over the top, like Air Pop Active Plus. It’s $150 mask that works with an app on your phone. I’m really not sure who really wants to spend $150 on a mask, but it’s there if somebody who does want to buy it. And then there were a lot of disinfecting robots. LG had a really cool one that uses UVC light to disinfect high touch, high traffic areas. And they’re going to market it to schools, to hospitals, to hotels, places like that. It rolls around and it disinfects on its own. So that is super cool. And Samsung had some disinfecting robots as well.
SEE: CES 2021: The big trends for business (ZDNet/TechRepublic special feature)
There was just quite an array of really cool products like that. But one that really stood out and I know this would have been that one, if everybody had been at CES in person, air taxis that always gets attention. And GM introduced the Cadillac E…I’m not sure how they pronounce it…but eVTOL air taxi, E-V-T-O-L. That is just really spectacular. They just did a virtual image of what it would look like and what it would be. They’re trying to get that created. It’s all electric with vertical takeoff and landing and it has speeds up to 56 miles per hour. So I thought that was super cool, but I could talk all day, but that’s just some of the stuff that we saw.
Karen Roby: Yeah. And as you mentioned, Teena, when you see so much, whether you’re in person or virtually, it all kind of starts to run together by the end of the week. There’s just so much-
Teena Maddox: Yes. I was running around virtually. I had 15 tabs open at once, so it was like the equivalent of running place to place in a taxi in Vegas like we usually do. And I still feel like at the end of the week, there’s like another thousand things I didn’t cover that I want to. So you still have that feeling, but it’s still a lot of fun and there’s still some more things that we’re wrapping up and writing about today because there’s a lot of really great things that come out and things that we’ll be writing about in weeks to come that just are things that were conceptual that may or may not be created, but still really inspired great stories out of us and others.
Tech to help us sleep better and PC innovations
Karen Roby: Yeah, I think so, too. And Steve, we talked several days ago on the front end of CES about what is this going to be like going to a virtual experience? We’re so used to that hands-on opportunity and when people collaborate but I think all in all, it turned out okay.
Steve Ranger: I think absolutely. In fact, I’m really amazed by the amount of energy and kind of enthusiasm and excitement there has been around CES and all the CES products. I mean who knows, maybe being virtual means we get to see more stuff, rather than be hiking from place to place. So then from hall to hall, actually just flipping between tabs like Teena was doing, means you get to see more stuff, which is great. Like her, I thought the robots is really interesting this year. Obviously, that really plays into what’s been happening in the last year or so.
One of the things I thought was quite interesting was a lot of the sleep tech, because on first glance, I thought, wow, this is just the tech industry finding something else they can encroach on and put a few chips into and resell us our own sleep again. But actually the more I thought about it was, well with loads of like exercise and things like that, we are present. So we kind of know if we’re out for a run and we have a vague idea of what we’re doing. When you’re asleep, you’re asleep. You have no idea what’s going on. So actually maybe sleep is one of the really good things to be measuring and trying to understand because it’s the whole chunk of the day when you really pretty much aren’t there.
So I think a lot of people are getting more sleep at the moment because they’re going outside less. So actually trying to understand what your sleep patterns are and trying to optimize that I think is a really good thing because actually, a lot of the time we’re not getting enough sleep, we’re not getting good sleep. So I started off rather kind of doubtful about some of these technologies. The more I think about them, the more I think they might actually have some interesting uses there.
The other thing that I saw that was really good, which comes every year, but I kind of like, is all the innovation around laptops and PCs. Certainly when we were speaking a while back, we were saying, there’s a renaissance of interest in the PC because many of us are at home working on PCs and we’re not using smart phones or tablets or whatever. And actually the PC, which had been kind of on a downward trend is actually back up again right now. And so it was nice to see a lot of companies playing with the idea of new screens or different screens.
So there was some laptops with a combined e-ink screen that you could use in different ways or a laptop with a secondary screen on the front that you could use alongside the keyboard. I guess none of these things are likely to take over the world simply because we are so used to the form factor we have with one screen, one keyboard. But I think it’s really nice to see there is still some innovation in what is a really traditional form factor that’s been around 30 years or longer. So I liked to see that as well.
SEE: The weird, the wacky and the marvelous at CES 2021 (TechRepublic)
And as Teena said, the robots is always good fun to look at some robots and for once they might actually have some actual uses this time in terms of healthcare and that kind of thing. So yeah, I think actually, I’m surprised by the levels of interest and innovation we saw and I think that’s a really good sign for the industry.
All-digital CES 2021 had its advantages
Karen Roby: Yeah, definitely, Steve. I agree with you. And especially with the sleep tech, interesting to see what we can learn that otherwise we’d have no idea about. And Bill, one of the things that’s been great about CES being virtual is that more people will have access to the information, otherwise that people can’t get to Vegas or don’t feel like they’re part of the show, but that was a big difference this year.
Bill Detwiler: Yeah, it is. And we’ve seen that with other events that have gone virtual in the wake of the COVID pandemic, is that it has enabled more people to participate, which is a good thing because, for the industry, for just society, for closing that technology gap and the only thing that I hope we see more in the future as we go back to the new normal of in-person events mixed with a digital event. Because let’s remember, most of these events always had a digital component. It’s just that it wasn’t the focus. Going forward now in the new normal, when we go back to in-person events, that there is a greater emphasis on the digital portion of the event and in allowing people to continue to participate, that maybe just can’t be there physically.
This was an interesting CES. Microsoft partnered with CTA to really sort of bring the virtual side of things to life. Microsoft has been on and off at CES, and this was a chance to showcase what they can do in that realm. Whereas maybe Microsoft competitors, Amazon through its home and consumer electronics brands and Google through its consumer electronics brands have been there in the past and Microsoft sometimes has been there and then has not been there. But this was really interesting to see the technology that they used to pull it off. And I think they did a pretty good job. I’ve been going for many, many years, and I will say that this definitely felt like more of a fire hose of information coming at you. There were a lot of products being released simultaneously. You had competing events happening at the same time or during the same week. So I think there’s a little more work to do around that for future shows, but all in all, I think it was a really solid virtual CES.
CES is a showcase for enterprise, as much as consumer, tech
Karen Roby: Yeah. Most definitely. I think that it will be interesting, like you said, to see how this year influences next year when assuming, we’re back in person.
Bill Detwiler: Yeah. And one of the things I thought was really interesting. Before we did this call this morning, I went back and I was looking at some old footage of CES from actually 1991. So 30 years ago. It was some footage from a show that I used to watch back here in the US on Public Television. There was show called The Computer Chronicles, which was all about the early days of computer technology in the eighties. And they were interviewing several people at CES. It was still in Vegas. This time they split it up. They actually had a summer and a winter CES. And of course, the big computer show at the time was COMDEX, which isn’t really around anymore like it was back then. And so they were interviewing Nolan Bushnell, who was co-founder of Atari, creator of Pong, and it was interesting to me that he was really talking about the merging of computers or computer circuits and chips at the time, with consumer electronics, because, up until then, consumer electronics were really about car alarms, cell phones.
They weren’t seen as computing devices. And in that transition from the late eighties into the nineties, you started to see people just thinking about electronics and consumer electronics as functional devices, tools that served a purpose. You could embed smarts into them and make them better products that helped people in their lives. And now we’re seeing the same trends, 30 years later, the same discussions. I know we talked about it earlier this week. You’re seeing that, except it’s not silicon that we’re talking about. Although we talk about that a little bit with miniaturization and power, low power chips, things like that, that allow us to put computers into your toaster. But they’re talking about the cloud now. We’re talking about 5G. We’re talking about enabling these technologies, these underlying enterprise technologies that put the smarts in all these smart gadgets that we have around our house that are being shown off at CES.
Karen Roby: Yeah. It’s always interesting and fun to look back on YouTube. It’s crazy when you look back at that old video and the quality of it and things like that. But man, we’ve definitely come a long way. And one thing too guys, before we jump off here, I think one thing that really raised some eyebrows is the president of Microsoft, Brad Smith coming on and talking about tech and how the industry has to remember ethics, improve in ethics, and that tech must be used for good. And in light of everything going on in the world, very poignant time for him to be speaking.
Bill Detwiler: Yeah. I think that is something that people sometimes don’t think about with technology, but tech is a tool like anything else. It’s either a benefit or a hazard.
Karen Roby: Yeah. Most definitely. All right. Well, we have got loads of coverage for you on ZDNet and Tech Republic from everything CES 2021. We hope you’ll check it out there. Thanks for being with us here today.
Glimpse of a blazar in the early universe
The supersharp radio “vision” of the U.S. National Science Foundation‘s Very Long Baseline Array has revealed previously unseen details in a jet of material ejected at three-quarters the speed of light from the core of a galaxy some 12.8 billion light-years from Earth.
The galaxy, dubbed PSO J0309+27, is a blazar, with its jet pointed toward Earth. A blazar is a feeding supermassive black hole in the heart of a distant galaxy that produces a high-energy jet viewed face-on from Earth. PSO J0309+27 is the brightest radio-emitting blazar yet seen at such a distance; it’s also the second-brightest X-ray emitting blazar at such a distance.
PSO J0309+27 is viewed as it was when the universe was less than a billion years old, or just over 7% of its current age.
In this image, the brightest radio emission comes from the galaxy’s core, at the bottom right. The jet is propelled by the gravitational energy of a supermassive black hole at the core and moves outward, toward the upper left. The jet seen here extends some 1,600 light-years and shows structure within it.
An international team of astronomers observed the galaxy in April and May of 2020. The researchers report their results in the journal Astronomy & Astrophysics.
“This research is important for understanding jets launched by feeding supermassive black holes,” says Joseph Pesce, a program director in NSF’s Division of Astronomical Sciences. “The observation allows for a more detailed assessment of differences between objects that are large distances from Earth (and in the early universe) and those relatively closer to Earth.”
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