Abstract: When technology is like a machine learning, it is full of misunderstandings. The following is a clear perspective on what machine learning can or cannot provide.
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When technology is like a machine learning, it can be misunderstood. The following is a clear perspective on what machine learning can or cannot provide.
Machine learning proved to be very useful and it is easy to assume that it can solve all problems and apply to all situations. Like other tools, machine learning is also useful in specific areas, especially for problems that have been bothering you forever, but you will never employ enough people to solve problems, or problems with a clear goal, but no obvious way to achieve it. .
Despite this, each organization has the potential to use machine learning in one way or another, because 42% of executives recently told Accenture that they expect artificial intelligence to be the backing of their innovation by 2021. However, as long as your vision can bypass the hype and avoid these common misunderstandings you will get better results – by understanding the mechanisms that machine learning can and cannot achieve.
Myth: Machine Learning Is Artificial Intelligence
Machine learning and artificial intelligence are often used as synonyms, but machine learning is the most successful technology from the research lab to the real world, and artificial intelligence is a broad field that covers computer vision, robotics, and Natural language processing and other fields, as well as other methods that do not involve machine learning, such as constraint satisfaction. Think of it as everything that makes the machine smart. These are not the kind of general "artificial intelligence" that ordinary people fear - things that can compete with people and even attack humans.
Pay attention to these buzzwords and be accurate. Machine learning is the result of learning patterns and predicting large data sets; the results may seem "smart," but its essence is related to the application of statistical data at an unprecedented speed and scale.
Myth: It's useful for data
Machine learning requires data, but not all data is available for machine learning. In order to train your system, you need representative data to cover the patterns and results that the machine learning system needs to handle. You need to have data that is not related to the pattern (for example, photos showing the content - all standing men and all sitting women, or all vehicles are in the garage, all bikes are in muddy grounds) because you The machine learning model you create will reflect those patterns that are too specific and look for them in the data you use. All the data used for training needs to be marked with the correct markup, and note the features that match the questions you have to ask the machine learning system. This requires a lot of work.
Do not assume that the data you already have is clean, clear, representative or easy to annotate.
Myth: You always need a lot of data
Recent major advances in image recognition, machine reading comprehension, language translation, and other areas have occurred because of better tools, computing hardware such as GPUs that can process large amounts of data in parallel, and a large number of tagged data sets, including ImageNet and Stanford QuesTIon Answering Dataset. However, because of a technique called transfer learning, you don't always need a lot of data to get good results in specific areas. Instead, you can teach the machine learning system how to use a large data set to learn and then migrate it to your own, smaller training data set. This is how the custom visual APIs of Salesforce and Microsoft Azure work: You only need 30 to 50 images to display the content you want to classify for good results.
Migration learning allows you to customize pre-trained systems for your own problems with relatively little data.
Myth: Everyone can create a machine learning system
There are many open source tools and frameworks for machine learning on the market, and countless courses show you how to use them. But machine learning is still a specialized technique; you have to know how to prepare the data and partition it for training and testing. You need to know how to choose the best algorithm and what heuristic algorithm to use, and how to turn it into reliable. Production system. You also need to monitor the system to ensure that the results are always relevant; whether your market revolution or your machine learning system is so good that you end up with a different customer base, you need to continue to test whether the model meets your problem .
It takes experience to thoroughly understand machine learning; if you are just starting to use the API, you can use the API to pre-train the model. When you get or hire data science and machine learning expertise to build a custom system, you can call it from the code. model.
Myth: All patterns in the data are useful
The survival rate of pneumonia in patients with asthma, chest pain or heart disease, and anyone in the last year is much higher than your expectations. In fact, it's good to have a simple machine learning system that automates the admission process so that they can go home safely (a rule-based system that can be trained on the same data as a neural network.) This one). Unfortunately, the reason they have such a high survival rate is that they are always admitted immediately because pneumonia is very dangerous to them.
The system has witnessed a valid pattern in the data; this is not a useful model for choosing who is admitted to the hospital (although it can help insurers predict treatment costs). What's even more dangerous is that unless you understand them, you won't know that these useless anti-patterns will appear in your data set.
In other cases, a system can learn an effective model (such as the controversial facial recognition system, which can accurately predict the orientation from the selfie), but it does not have a clear and explicit explanation, so it is not useful (in the In this case, the photographs seem to show social cues such as gestures rather than anything born.
The "black box" model is efficient, but it does not clarify what model they actually learned. A more transparent, understandable algorithm like the Generalized Addi- tion Model can understand the model's learning content more clearly, allowing you to decide if it is suitable for deployment.
Myth: Reinforced learning can be used at any time
Almost all machine learning systems used today use supervised learning; in most cases, they have received training in explicitly marked data sets prepared by human participation. Managing these data sets takes time and effort, so people have a great interest in unsupervised learning, especially reinforcement learning (RL)—here, agents learn through trial and error, through The environment interacts and is rewarded for correct behavior. DeepMind's AlphaGo system used reinforcement learning and supervised learning to defeat the top-level Go players, and the system built by Carnegie Mellon's team, Libratus, used reinforcement learning and two other artificial intelligence techniques to defeat Noble Hold'em. Part of the best poker player in the world (with a long and complicated betting strategy). Researchers are using machine learning to perform intensive tests on everything from robotics to security software testing.
Reinforcement learning is not common outside the research area. Google uses DeepMind to learn to reduce data center temperatures more efficiently, thereby saving data center power; Microsoft uses a special version of Reinforcement Learning, known as a contextual bandit, which customizes MSN.com's visitors. Headlines. The problem is that there are very few real-life situations with easily discoverable rewards and instant feedback. When Avatar uses multiple actions before anything happens, distributing rewards is especially tricky.
Myth: There is no deviation in machine learning
Since machine learning learns from data, it replicates any deviations in the data set. Searching for a CEO image may show a photo of a white male CEO because more CEOs are usually white males. But it turns out that machine learning can also amplify deviations.
COCO data sets often used to train image recognition systems have photos of both men and women; however, more women are shown next to kitchen equipment, and more men are shown with computer keyboards and mice or tennis rackets and skis. Training the system on the COCO, it will link men and computer hardware more strongly than the statistics in the original photograph.
A machine learning system may also add bias to another machine. Train such a machine learning system - it has a popular framework of characterizing words as vectors - to represent vectors of relationships between words. It will learn like "man to woman as computer programmer to housewife" or doctor Nurses are as stereotyped as the boss is at the front desk. If you use a system that translates languages ​​that have pronouns like him and her (such as English) into languages ​​that have neutral pronouns (such as Finnish or Turkish), then “they are doctors†will change Into "He is a doctor", "They are nurses" becomes "She is a nurse." (Annotation, the last sentence is a bit puzzling, but it is not difficult to understand the combination of the new pronoun TA that was born in the Chinese online language. Because it is not sure of the gender of the person who is alleged, writing TA, not his or her, is equivalent to English. He or she, and in some languages, the plural is used to represent neutral sex, such as they means he or she, which can be understood as they=TA. It is not difficult to understand the new Chinese words.
It's useful to get similar advice on shopping websites, but it can create problems when it comes to sensitive areas, and it can generate feedback loops; if you join the Facebook group against vaccination, Facebook's recommendation engine will suggest other concerns about conspiracy theories. Or think of a flat group of earth.
It is important to understand the deviations in machine learning. If you cannot eliminate the bias in the training data set, use techniques such as gender association between regularized word pairs to reduce bias or add irrelevant items to the proposal to avoid "filter bubbles."
Myth: Machine learning is only used for good deeds
Machine learning provides powerful features for anti-virus tools and looks at the behavior of new attacks so that they can be discovered as soon as they appear. But similarly, hackers are also using machine learning to study the defenses of antivirus tools and to make targeted phishing attacks on a large scale by analyzing a large amount of public data or analyzing the success of previous phishing attempts.
Myth: Machine learning will replace people
People often worry that artificial intelligence will take away their jobs. It will certainly change the work we do and the way we do things; machine learning systems can improve efficiency and compliance and reduce costs. In the long run, it will create new roles in the business and make some current positions look dated. But many of the tasks automated by machine learning have never been possible before, either because of complexity or because of scale. For example, you cannot hire enough people to view every photo posted on social media. To see if it has your brand features.
What machine learning has begun to do is create new business opportunities, such as improving customer experience through predictive maintenance, and providing advice and support to business decision makers. Like previous generations of automation, machine learning can liberate employees and enable them to apply their expertise and creativity.
