Machine learning, capsule networks, and artificial intelligence are no longer the subject matter of science fiction. Instead, they are the driving forces behind billion-dollar industries, such as autonomous-driving cars, medical diagnosing, and anti-terrorism. That said, as far ranging as the applications of machine learning are, there are distinct trends to watch. These trends are important in that they impact finances, society, and even the judiciary system. In fact, many world leaders now emphasize that the person, state, or nation to control AI and machine learning will control the world.
Top 10 Machine Learning Trends
1. Military autonomy
Machine learning has reached a point that fully autonomous systems will soon control military ships and even bases. Via behavioral patterning, machine learning can estimate the probability that an approaching force is friendly or belligerent. In fact, a few ground vehicles equipped in this manner are controlled completely by machine learning to the degree that little human oversight is actually needed. In these instances, machine learning has powered artificial intelligence enough that an AI-powered sentry can detect, assess, and even fire upon a threat with deadly force. The trend here is one of humans becoming more comfortable with machines making lethal decisions. As comfort levels rise, the number and complexity of autonomous military units are also expected to rise.
2. Security in the home
AI-driven home-security systems are not quite common, but they are on the rise. For instance, specific components, such as smart locks can communicate with your smartphone. However, these systems are slated for replacement by monitoring systems that can see your home via video, detect a threat, and notify authorities. Additionally, machine learning is predicted to transform home security and in-home personal security in that systems will be able to predict a threat based on interpreting behavior, such as abuse or even kidnapping.
Machine learning has often been restricted to mathematical calculations, statistical analysis, and game-based performance. However, machine learning is now able to correctly identify real-world objects. How these objects are interpreted depends on the specific use of the robot or software, but vision-based machine learning can identify such things as people, cats, or terrain. Consequently, sight-powered AI is on the rise, which is expected to impact home security, driving, and healthcare.
4. No more black boxes
Much of what machine learning accomplishes becomes unknowable at various points of the process. For instance, the machine learning programs that develop super-human reasoning in various games make moves that humans describe as alien. The reasons behind the moves are simply not expected, and reverse engineering such decisions are nearly impossible. Simply put, humans are usually in the dark when it comes to understanding how AI operates. This is changing.
For instance, research is being conducted on the possible paths programmers can take to hard wire transparency into the machine learning. Additionally, machine learning is being coded in a way so as to make the algorithms more understandable. Finally, rudimentary efforts are underway to make machine learning report on the process behind performance.
Everyone knows that jobs involving repetitious actions are being taken over by robots, smart or dumb. However, machine learning has also made certain white-collar professions vulnerable to displacement. For instance, x-ray interpretation is something machine learning is making advances in, putting the job of x-ray technicians in the cross hairs of AI displacement. Similarly, attorneys are expected to be displaced by machine learning capable of predicting the best pathways to winning a suit. Currently, this type of AI that predicts legal strategies is overseen by partners who also keep a staff of attorneys on the payroll. However, as partners become comfortable with AI decisions and as AI becomes routinely successful in making legal decisions, jobs for interns and junior-level attorneys are predicted to decline.
6. Internet of things
Currently, devices that can connect to one another are deemed smart. However, this concept of smart is evolving as machine learning is being applied to the so-called internet of things. For instance, different companies have developed digital sentries that listen in on people via their cell phones, televisions, and speakers. Alexa and Siri are two such sentries, and they are specific to Amazon and Apple, respectively. However, a more general personality is coming in the form of voice-based request software that will be connected to the internet of things. If you have a coffee pot, you can ask this entity to make your coffee. If you are on your way to the office, you can ask that your car drive you to work. As machine learning develops, the currently segmented personalities will be connected and be able to converse and share data. The result will be a generalized sentry that governs many of today’s routine tasks.
7. Cyborgs and general augmentation
On the rise are gadgets that can monitor our biological data and respond accordingly. For instance, individual hackers are creating devices connected to people, and these devices augment faulty biology by delivering insulin for people with diabetes. However, AI is also being used to help broaden people’s perception via augmented glasses. Augmented apps in smartphones are becoming more common, and machine-learning brain-computer interfaces allow quadriplegics to talk and interact with games. Although the industry is in its infancy, progress is doubling every year or so. In 2020, Elon Musk is already planning to test brain implants that directly link brains to computer software.
8. Attuned AI friendship
Machine learning is employed by a variety of retail companies to make suggestions to prospective customers. However, machine learning is becoming much more adept at serving current needs of people wanting diversion or entertainment. For instance, Netflix employs machine learning to understand what kind of shows people like. Making suggestions for sales is one thing. Making on-the-fly suggestions to current customers is quite another because the suggestions are not a sales tool so much as they are a means of satisfaction. The machine learning that powered this suggestion service worked so well that it was able to save Netflix over $1,000,000,000 in lost revenue due to subscription cancellations.
This type of personal care is currently passive. For instance, the interaction with people is text based, and the ability of the AI to be attuned to personal preferences comes from monitoring clicks on a screen. However, machine learning will soon morph into verbal interaction that makes the AI more personable. As machine learning becomes more adept at understanding what people want in specific instances, AI will become less of a sales tool and more of a digital friend.
Natural language processing (NLP) is on the rise, and it has made impressive strides that allow machines to construct textual information based on a random initial input. In fact, one NLP can write poetry, stories, and news articles that are disturbingly convincing. Upcoming progress is slated to become conversational, allowing companies to serve specific needs of customers with questions about a company’s products or services.
10. Politics and fake news
Deepfakes are on the rise, and companies and governments are bracing against the potential confusing impact such technology will have on populations. For instance, machine learning has reached a point that it can listen to audio data from someone and then create nuanced speech patterns that very closely match the sound and speech patterns of the actual person.
Additionally, machine learning is becoming more adept at being able to analyze hundreds of photographs of a single person. After analyzing the images, AI can then reconstruct video-quality depictions of the person. The result of these two technologies is something called a deepfake. Combined, deepfake audio and video will allow AI to construct seemingly authentic messages from celebrities, government leaders, or even regular folk. Moreover, the technology is expected to be entirely convincing within the next 12 months.
In terms of delivery, it is expected that such fake media will be delivered to people via social media as social websites are currently ill equipped to detect and combat deepfakes as long as they seem to fall within the company’s terms of service. Even when such media is finally uncovered and removed, the intended audience will likely already be influenced in one direction or another.
10 Statistics About Machine Learning Technology
1. Equal-opportunity displacement – Customer service across all fields is expected to shift to automated AI imbued with machine learning. Current estimates are that between 70 percent and 90 percent of all basic customer-service requests will be handled by AI.
2. Mastery at 1,000,000 repetitions – Within humans, mastery of a subject comes after approximately 10,000 trial runs. Machine learning achieves mastery somewhere between 30,000,000 and approximately 1,000,000 trial runs. As software and computers increase in processing speed, machines will reach this milestone much faster.
3. Corporate shift – 20 percent of all companies are expected to shift to AI-powered processes.
4. $120,000,000 industry – Machine learning is expected to become a $120,000,000 industry by 2025. This surpasses the movie, coffee, and LED lighting industry.
5. 38 percent of jobs replaced in next 10 years – Although 85 percent of jobs in customer service are slated to end, 38 percent of all jobs are projected to be gone within the next 10 years.
6. 10 percent of jobs created by cognitive entities – Fortunately, 10 percent of jobs lost will be created in the cognitive-entity industry, which includes machine learning, AI, and robotics.
7. Jobs for the top percent – Currently, it is estimated that for the coming technological job surge, fewer than 10,000 people are qualified to take on the responsibilities required by jobs in AI, machine learning, and other jobs that do not yet exist. Phrased differently, based on a global population that will soon hit 8,000,000,000, a skills gap will arise in which only the top .00000125 percent of people will be able to obtain tech-based jobs.
8. Breast cancer detection – Currently, machine learning can predict breast cancer with an accuracy rate of 89 percent.
9. More meaning – As people are freed from monotonous and meaningless jobs, 72 percent of people will be able to find more meaning in their lives.
10. Save money by talking – Chatbots are predicted to save companies $8,000,000,000 in costs related to employment.