Artificial Intelligence, Future
How Will Artificial Intelligence Change the Future?
Artificial intelligence is gradually increasing our levels of productivity. These include things like speech recognition technologies, which power chatbots and personal assistant devices. Also, users are interested in automated machine learning technologies and business applications with built-in AI mechanisms. There is a growing demand for AI platforms delivered as a service and related cloud services. Having said this, there are certain applications, such as fully autonomous vehicles, which will not be available any time soon.
The latest AI research from Gartner shows a wide variety of AI applications in enterprises. This should come as no surprise since its own surveys in 2019 showed that the share of organizations that have implemented AI increased compared to last year from 4% to 14%. Since AI is becoming mainstream, we decided to look at how it will impact the future. But before we look ahead, let’s take a look back and look at a brief history of AI.
Brief History of Artificial Intelligence
The development of AI systems was made possible only after the advent of modern computers after World War II. In the 1950s, scientists from various fields began to consider the possibility of creating an artificial brain. Then research in the field of neuroscience showed that the brain is a neural network, and Alan Tuning suggested that any kind of computation can be represented in digital form, and in 1951 the first SNARC neural network was created by graduate student Marvin Minsky. By 1950, Turing developed a test that determines the level of similarity of machine actions with human consciousness, later called the Turing test. The name “artificial intelligence” was first used at the Dartmouth Conference in 1956, at the same time the scientific discipline “Research on Artificial Intelligence” appeared.
Subsequently, many machines were created that understand human speech, are able to maintain conversations on given topics, robots playing board games: the famous match between the computer and Kasparov in chess ended in the victory of the machine. Now artificial intelligence occupies an important position in the development of science, especially within the framework of the concept of the Internet of Things, because it is not enough just to collect data, it is necessary to process, analyze and act in cases where a person cannot do this.
Now that we received a brief history of AI, let’s take a look at what the future holds.
Autonomous Vehicles
Since we mentioned in the beginning that it will be a while before autonomous vehicles are available, let’s take a look at this technology first. If you would like to learn more about some of the difficulties in developing autonomous vehicles, this article does a good job of explaining it. One of the biggest benefits and promises of self-driving cars is that they will make it easier and safer to move people and objects as well. The basic idea is that some people are really good drivers, but many people are not, and a world full of self-driving cars could potentially be much safer than the world of human drivers.
Self-driving cars from companies like Waymo can be a huge benefit for people who can’t or don’t want to get a driver’s license; people with disabilities who make it difficult or impossible to drive a car; and older people who have poor eyesight or slow reaction times make them unable to drive safely. Driverless technology can also come in handy in emergencies. For example, if a driver becomes ill or disabled and unable to drive, a vehicle equipped with self-driving technology can help them become mobile and increase their independence. All of the issues that are preventing autonomous vehicles from becoming mainstream can be overcome with more training data. There will also be an increased need for quality data annotation that helps preparing the datasets and training the machine learning algorithms.
Conversational AI
Conversational AI refers to some widespread technologies, like voice assistants, chatbots, that interact with people in a natural humanlike way. Creating such an AI system is very complicated, but extremely useful for almost all industries. Evgenia Khimenko, the CEO of Mindy Support, a company that provides data annotation services for companies creating conversational AI systems, on how the process of creating this system looks like:
“When we look at the process of creating a conversational AI system, or any AI product for that matter, it resembles a pyramid structure. At the base of the pyramid we have all of the training data needed to create the system. However, all of this raw data needs to be prepared through various processes that fall under the umbrella term data annotation. As far as conversational AI is concerned, this includes Natural Language Processing, which requires thousands and thousands of texts to be annotated with techniques like text classification, sentiment analysis and simply labeling key phrases.
All of this annotated data would be fed into the system, which is the next stage in the pyramid, where the data and technology begin to interact. The data flows through the system and when it has been sufficiently processed and trained, we reach the analytics level, which is where insights can be gleaned from the data. Now, if we are not satisfied with simply extracting insights from the data, but we want the system to take this analysis and apply it without any explicit programming, this is the next stage, which is machine learning. The machine learning algorithms are continuously improving through experiences, essentially learning as they go.
AI is the pinnacle of the pyramid, the holy grail sort of speak. This is the ultimate stage where the machine can replicate the human thought process. Therefore, even though machine learning is a big part of AI, artificial intelligence is what allows machines to produce human capabilities like language comprehension.”
Artificial Intelligence in Healthcare
AI and machine learning have tremendous potential in healthcare as healthcare organizations collect and process vast amounts of medical data and other information. Machine learning can help organizations analyze large libraries of data and identify the most relevant medical data in the context of problem solving and refining treatment options or business processes. Advanced analytics enable better decision-making, as well as stimulate the search for new ideas and sources of competitive advantage. A lot of companies have already started using AI to transform healthcare and new products are being developed every year.
Before artificial intelligence was used to process health information in the 2000s, predictive health care models could only account for a limited number of variables in well-prepared health data. Modern machine learning tools that use artificial neural networks to study extremely complex relationships or deep learning technologies often surpass human capabilities when performing medical tasks. Artificially intelligent systems are capable of solving complex problems in modern clinical services. This includes things like helping reduce human error to more advanced functionalities such as predicting the possibility of a patient developing cancer.
AI in Agriculture
AI in agriculture is not only software solutions that allow farmers to predict crop yields in the fields using aerial images or video to monitor the condition of livestock on farms, but also robotic systems that can significantly reduce the burden on farmers and free them from routine operations.
However, despite the growing number of examples of technology adoption in agribusiness companies, a number of challenges remain in the industry that require business and government to work together. These include stimulating demand for AI technologies, the high cost of implementing individual solutions, the lack of domestic AI products and solutions, implementation risks and many other considerations.
You can expect to see the use of AI in both crop and livestock production, where technologies such as analyzing the pattern of people and analyzing the pattern of animals are relevant. Analysis of animal patterns allows you to proactively respond to health problems, track diet and treatment.
Smart Home
The vision of the house of the future — there have been countless sci-fi movies created about this topic as well as many books and articles have been written about this. Today, the dream of science fiction writers — the Smart Home, in which computers are responsible for all vital functions, is becoming a reality. We can summarize the main characteristics of such a house, with a few words: comfort, functionality, and energy efficiency. Smart Home adapts to your habits. It will turn off all the lights in your house when everyone is asleep, turn off unnecessary electrical outlets, switch it to the economy mode, close the doors and even guard your house from burglars. It can heat up your home in the morning, make coffee, wake you up with your favorite melody and watch over the safety of your children. Your home will be much more secure, since it will watch over every window, every door and it can also be the perfect butler he i.e. announce the arrival of guests. Keep in mind, this is only a small sampling of all the possibilities.
A lot of such devices are already commercially available so you can start enjoying such convenience today. However, in the future we will see more personalization and more advanced smart home technologies.
Robotics
AI robotics are becoming widely used by companies across industries to accomplish a wide array of tasks. A lot of these companies are using robotics to increase the level of automation, but there are many other that are reimagining the way work is getting done to maximize the value of both people and machines, creating new opportunities to organize work more efficiently and redefine the skills and careers of the workforce. As more organizations try to embrace these technologies, the market for AI robotics tools is booming. Leading companies such as Microsoft, IBM, Facebook and other tech giants are actively investing in this area.
Today, a lot of the leading companies would agree that these technologies are most effective when they augment human capabilities rather than replace them. For example, Amazon is using robots to cut the training of seasonal employees to less than two days. Walmart recently rolled out virtual reality technology to improve in-store learning and effectively simulate customer experiences. Manufacturers such as Airbus and Nissan are looking for ways to use collaborative robots or “co-robots’’ that work side-by-side with workers in factories.
We can expect this combination of humans and robotics to continue into the near future, with only monotonous and routine tasks becoming automated.
AI in Security
Today security cameras are widely used to defend against burglars, but there are so many other functionalities that such cameras can serve with the addition of AI. Facial recognition technology can be used to recognize homeowners and also can be used in the office to authenticate employees and grant them access to the premises. This would make things like keys and ID cards obsolete. In the future, you can expect such technology to be applied in the banking sphere as well. We are already able to log into our mobile banking with technology like Face ID, but this will be further expanded to allow for a wide range of banking services to become safer so you can make a withdrawal at an ATM, open a new credit card and many other services.
What We Can Expect in the Future
Artificial intelligence is now the leading global technology trend of the future. AI is at the heart of today’s technological revolution and is expected to revolutionize not only the information technology industry, but also the automotive, banking, agricultural and medical sectors. The main benefit for business is not total automation, but skillfully combining machines with people. Experience shows that while there are many routine and mundane jobs that can be automated, there is still room for human workers in the future.
In banks, retail, industry and energy, fully automated data collection and primary analytics will increase the demand for employees with managerial competencies and the ability to solve non-standard tasks. The growth in life expectancy increases the demand for professionals in the medical field, who occupy half of the top ten in the ranking of the most in-demand professions of the future according to Kiplinger. If automation affects high-precision surgical procedures and primary diagnostics, then communicating with patients, just like today, will require skills that artificial intelligence is not yet capable of.
If people and machines work together, then more jobs will be created associated with robots and artificial intelligence.
