It is essential to understand what exactly machine learning is and how it works in order to understand the affects it has and will have on society. In basic terms, machine learning is a computer program that can learn and adapt to new data without human interference. It is a field of artificial intelligence that keeps a computer’s built-in algorithms current, regardless of changes in the worldwide economy. As more information it is given, the more it learns and the more accurate it becomes with the result. Therefore, it recognizes patterns and will give updates as relevant data is fed through. Deep learning is a subset of machine learning, which helps the algorithm learn without a human programmer. Through deep learning, information filters through a hierarchy that starts simple and gradually becomes more complex and specific. As the data is going through each level, the algorithm is able to determine what the object or data point belongs in what category. The process behind deep learning is essential to the success of machine learning. Since there is a grand amount of information that the algorithm must analyze to come to a conclusion, a human programmer would not be able to sit there and go through every single point – that would be extremely expensive and time consuming. Many industries and companies are beginning to use machine learning, to cut costs and become more efficient. Some of those industries include transportation, advertisement, medical, education and entertainment.
Transportation & Advertising – Antoneta Sevo
One industry that is being hugely affected by machine learning is transportation, particularly by autonomous vehicles. Though taxi companies like Waymo and Uber are implementing this technology within their services, companies like Tesla are creating vehicles that will be available to consumers for purchase. Tesla has introduced a system called Enhanced Autopilot that allows the driver to sit in front of the wheel and do nothing while the vehicle operates itself. The system includes features like active cruise control, forward collision warning, match speed based on traffic conditions, change lanes without the driver’s input, merge on and off highways and park itself. One main goal of Tesla is to successfully have a car drive itself across the country from LA to New York. Machine learning works by interpreting the ever-changing scenery detected by the sensors surrounding the vehicle. Once the technology is perfected within the industry, there may be no limitations to the future of driving.
As machine learning continues to be introduced into different industries, the amount of information being collected about users and consumers continues to increase. These algorithms are created to handle and sift through large amounts of data in order to identify a pattern and produce a result based on the patterns. This is beneficial to companies for advertising purposes so they can recognize habits of consumers. Essentially, companies track your browsing history to suggest products or other websites. They are also able to figure out how much the user can afford and when they might be able to afford it based on their demographics and when they usually purchase products. Since it would be time-consuming and expensive for a human to find those patterns, an algorithm is put in place and works in real-time. It is a much more efficient way to produce targeted advertisements. The more a user searches and buys, the more information the program collects, and the more accurate the advertisements become. By using machine learning, Target knew a teenager was pregnant before her father did. The algorithm noticed a pattern in her internet searches and sent advertisements designed for pregnant women to her house. That was how her father found out. Machine learning can collect an endless amount of data in order to have accurate predictions, and in some cases, they can be too accurate.
With new technology comes ethical issues and implications. When it comes to autonomous vehicles, many things can go right and there would be a smooth ride, but many things could go wrong as well. For example, the algorithm may make the wrong decision, misinterpret an object, or can be hacked, causing an accident. In order to avoid the program from making the wrong decision or misinterpreting, quantum computing must be implemented so the algorithm could work faster. It is important for companies to consider full transparency, so people understand how the car operates and how it is protected. This also means certain types of cybersecurity and encryptions should be put in place to protect the car from being hacked and causing harm to people on the road. Most car companies do not operate in an ethical manner now, however, they should begin to think differently in the future. There are also many different ethical issues that arise through the advertising industry. Once people find out they are being tracked and targeted, they begin to feel uneasy, as they should. Companies should allow consumers to opt out of their services so users will feel more in control of their information. Also, it would be beneficial if consumers were more aware in order to identify what these big corporations are tracking so they can do things differently to take themselves out of being tracked. Consumers can use a Virtual Private Network, use a search engine that does not track them like DuckDuckGo, or they can choose to use an opt out service. There are plenty of options, the first step, however, is to be aware that something is wrong.
The final scare that comes along with new technologies in machine learning and Artificial Intelligence is unemployment. Many articles talk about how many blue-collar jobs will be obsolete in the coming years and most of them are not wrong. It is a topic that those who want to be employed have to know about. Some jobs that may be affected in the future are schoolteachers, taxi or commercial drivers, positions in the medical field, etc. This technology has the potential to affect an abundant amount of jobs across plenty of industries, which means a solution must be in place before mass unemployment occurs. A short-term solution may include technology companies implementing training programs for current employees to learn the new (and necessary) skills. Another solution might be considering a universal basic income for those who are unable to find work or for everyone if machines and Artificial Intelligence take a significant amount of jobs. In time, as more and more jobs disappear, we, as a society, will have to redefine the definition of work. Millions of jobs will disappear and we will have to accept the change. In order to survive the inevitable societal and economic change, we need to accept that it is happening now. The next step is to begin learning in an exponential way instead of a linear way. The way our world functions now will certainly not be the way it functions in the future and we need to prepare to face it head on.
Medical & Education- Mayra Luna
Machine learning is all about data recollection and data processing. Its capability of analyzing huge amounts of data will eventually replace a great number of human jobs, even those that require a higher education. However, as explained by Medtronic CEO Omar Ishrak, the real value of artificial intelligence is in making more efficient use of human resources in healthcare. For example, IBM’s Watson can read 40 million documents in 15 seconds, optimizing time and performance. Healthcare has loads of data: Test results, consultation notes, scans, appointment follow ups, etc., creating an ideal environment for the use of Machine Learning (More data, better results).
The ability of computer systems to assume tasks for humans has improved efficiency in healthcare. Now hospitals are getting into the game, deploying AI to take on challenges from diagnosing patients more quickly in the emergency room. Other technology developers, are focusing on software that can read CT scans and other medical images and then suggest the most likely diagnosis by reviewing similar images stored in patient databases. And these programs can accurately process these tasks far faster than human technicians. For example; Stanford researchers have developed an algorithm that offers diagnoses based off chest X-ray images. It can diagnose up to 14 types of medical conditions and is able to diagnose pneumonia better than expert radiologists working alone.
Machine learning can help healthcare executives and caregivers with things like precision medicine. Precision medicine is “an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person.” This approach will allow doctors and researchers to predict more accurately which treatment and prevention strategies for a particular disease will work in which groups of people. It is in contrast to a one-size-fits-all approach, in which disease treatment and prevention strategies are developed for the average person, with less consideration for the differences between individuals. For example, tech giant Microsoft wishes to improve health care using machine learning and AI. As part of this initiative, Microsoft is expanding into cancer research and treatment, and its approaching cancer cells as if they were a glitch in a computer system.
Though ML promises to drastically improve the efficiency and effectiveness of healthcare, when it comes to predicting, diagnosing, and treating medical conditions there are many concerns: Data quality, Manipulation risk, Obscured logic
Four factors should be present to improve accuracy and overcoming risk: Confidence scores, Complex rules, Clinical data, Natural language processing
We must understand that machine learning is a powerful tool, not a complete solution. There is no substitute for a skilled physician’s expertise, however with the right data, ML can certainly help accelerate diagnosis, treatment, and program development.
Machine learning systems can give teachers more free time to actually teach and mentor on a more personalized level, instead of focusing on the never-ending grading and lesson planning. In addition, the current learning system consists of a “one size fits all” model. We are in desperate need of a system where the educational experience can be personalized to each students abilities and needs. Machine learning could address these issues by collecting and analyzing the data generated by students, identifying meaningful patterns and transforming that information into structural knowledge. In other words, when the student interacts with a digital learning platform, the machine learning system can accurately predict and better assess that person’s educational level, being able to tailor specific material for that individual.
Specific roles and examples of Machine Learning and its use in education: Content analytics, learning, analytics, dynamic scheduling platforms, grading systems, process intelligence, predictive analysis.
The ethical issues may include:
- Unemployment: Labor is concerned primarily with automation. As we continue to implement machine automation, certain jobs (predictable physical work) could disappear. While this might sound like something bad, we must also keep in mind that new jobs will be created due to this disruption. The real question is how are we going to educate people for these new jobs?
- Inequality: By using artificial intelligence, companies can drastically reduce the reliance on human workforce, which means people will work less hours (therefore make less income, broadening the labor gap).
- Humanity: Humans are limited, while machines have unlimited resources. How will machine learning affect the way we behave and interact with others? Will we soon be interacting with machines as if they were humans? Will machines adapt to cultural norms?
- Algorithmic Bias: Blind spots or biases in the algorithms could lead to discrimination against certain types of people (As seen in AI systems that can tell if you are homosexual).
Entertainment- Olivia Finan
The role that machine learning plays in entertainment is very interesting. A common belief among the class is that entertainment is an industry that will more or less remain untouched in the coming years by the disruptive technology. However, research shows that this may not be the case. We have already begun to see drastic changes in the industry throughout the years, specifically in music and cinema. AI systems are being created and coded to be able to sift through video footage and put together a 10 minute video using several clips in under and hour. This is impressive timing when you consider how long it takes to watch hours of footage, edit, tag, and compile all those clips into one succinct video my hand.
Filmmaker Oscar Sharp and technologist Ross Goodwin fed a machine learning algorithm with a bunch of Sci-Fi movie scripts to see what new script it would spit out.
The trends that we think will see in entertainment are not necessarily what is coming. How this content is created is what will change how the industry progresses through the coming years and how it is affected by disruptive technology. The entertainment industry is a prime example of how we must pivot and change how we do things rather than change what we do.
More so then ethical issues, legal issues come into play when talking about the entertainment industry. Laws regarding copyright infringement are always looked at thoroughly in this particular industry. However, ethical issues tend to revolve around the consumer rather then the company or a performer. For example, at live events, issues about safety come into play. This means that a major role would be ensuring that concert or festival goers are safe at the venue. This is just one example of what time of ethical dilemmas companies have to deal with in the entertainment industry. What these companies now have to begin thinking that the ethical implications that once were may not be the same anymore. We may not be having live concerts, there might be other ethical issues that arise, and that is what the industry predicts.
The entertainment industry is not exempt from responsibilities associated to employment. However, if there is a change in music, for example, and humans are no longer the ones producing it then what happens to the people who world at record labels, or what happens to the artist themselves? This section of the machine learning project will explore all the changes the industry sees coming in the future and how the major players are beginning to prepare and adjust to the changes.