Impact of Artificial Intelligence on our daily lives...

Artificial Intelligence (AI) is becoming an important part of our daily life, in social as well as the business environment. From healthcare to the military, this technology is being introduced in all the sectors to reduce human effort and give an accurate and faster result.
We are fortunate to live in this generation, which is full of technological advancements. Now we live in a time where a lot of work is taken over by machines & software. AI has a special place in all the advancements made today. As we know that AI is the science of computers and machines developing intelligence like humans. In this technology, the machines can do some of the simples to complex tasks that we as humans need to do regularly.
The AI systems are capable enough to reduce human efforts in numerous areas. To conduct different operations in the industry, many of them are using artificial intelligence to create machines that perform various activities regularly. The artificial intelligence applications help to get the work done faster and with accurate results.

General Advantages

While AI has been very useful in many domains like healthcare, automotive, etc, there are some general advantages you get in any field by applying AI. Let us have a look at some of them:
Computed methods for automated reasoning, learning and perception have become a common phenomenon in our everyday lives. We have our Siri or Cortana to help us out.
The smartphone is an apt and everyday example of how we use AI. We are also hitting the road for long drives and trips with the help of GPS. In utilities, we find that they can predict what we are going to type and correct the spellings. That is machine intelligence at work.
When we take a picture, the AI algorithm identifies and detects the person’s face and tags the individuals when we are posting our photographs on social media sites.
AI helps us in reducing the errors and the chance of reaching accuracy with a greater degree of precision. It is applied in various studies such as exploration of space.

Intelligent robots are fed with data and are sent to explore space. Since they are more resistant and have a greater ability to endure the space and hostile atmosphere due to their metal bodies. They are built and acclimatized in such a way that they cannot be altered or get damaged or malfunction in a hostile environment.
Repetitive tasks are monotonous in nature can be carried out with the help of machine intelligence. Machines think faster than humans and can be put to multi-tasking. Machine intelligence can be employed to carry out dangerous tasks. Their parameters, unlike humans, can be adjusted. Their speed and time are calculation based parameters only.
When humans play a computer game or run a computer-controlled robot, we are actually interacting with artificial intelligence. In the game we are playing, the computer is our opponent. The machine intelligence plans the game movement in response to our movements. We can consider gaming to be the most common use of the benefits of artificial intelligence.
Artificial intelligence and the science of robotics can be put to use in mining and other fuel exploration processes. Not only that, these complex machines can be used for exploring the ocean floor and hence overcome human limitations.
Due to the programming of the robots, they can perform more laborious and hard work with greater responsibility. Moreover, they do not wear out easily.
Highly advanced organizations use ‘avatars’ which are replicas or digital assistants who can actually interact with the users, thus saving the need for human resources.

For artificial thinkers, emotions come in the way of rational thinking and are not a distraction at all. The complete absence of the emotional side, makes the robots think logically and take the right program decisions. Emotions are associated with moods that can cloud judgment and affect human efficiency. This is completely ruled out for machine intelligence.
Unlike humans, machines do not require frequent breaks and refreshments. They are programmed for long hours and can continuously perform without getting bored or distracted or even tired.

Domain-wise Advantages

‘AI for Good’ is a United Nations platform. It is centered around an annual Global Summit that promotes the exchange on the beneficial use of AI by building specific projects. The purpose of organizing global summits that are action-oriented, came from an existing discussion in AI research being dominated by research streams such as the Netflix Prize (improve the movie recommendation algorithm). The AI for Good series aims to bring forward AI research topics that contribute towards more global obstacles, in particular through the Sustainable Development Goals, while at the same time avoiding typical UN-style conferences where results are usually more abstract.
The main purpose of healthcare AI applications is to examine relationships between prevention or treatment techniques and patient outcomes. AI programs have been built and implemented to practices such as diagnosis processes, treatment protocol development, drug development, personalized medicine, and patient monitoring and care. Many medical institutions have developed AI algorithms for their departments.
Large technology companies and even startups have also developed AI algorithms for healthcare. Additionally, hospitals are looking to AI solutions to support operational initiatives that increase cost-saving, improve patient satisfaction, and satisfy their staffing and workforce needs. Companies are also developing predictive analytics solutions that help healthcare managers improve business operations through increasing utilization, decreasing patient boarding, reducing the length of stay and optimizing staffing levels.
In agriculture, new AI developments show advances in gaining yield and to increase the research and development of growing crops. AI now predicts the time it takes for a crop like a vegetable to be ripe and ready for picking thus increasing the efficiency of farming. These advances go on including Crop and Soil Monitoring, Agricultural Robots, and Predictive Analytics. Crop and soil monitoring uses new algorithms and data collected in the field to manage and track the health of crops making it easier and more sustainable for the farmers.
More specializations of AI in agriculture are one such as greenhouse automation, simulation, modeling, and optimization techniques.
Due to the rise in population and the increase in demand for food in the future, there will need to be at least a 70% boost in yield from agriculture to support this new demand. More and more of the public perceives that the adaption of these new techniques and the use of AI will help reach that goal.
The Air Operations Division (AOD) uses AI for the rule-based expert systems. The AOD has use for artificial intelligence for surrogate operators for combat and training simulators, mission management aids, support systems for tactical decision making, and post-processing of the simulator data into symbolic summaries.
The AOD also uses artificial intelligence in speech recognition software. The air traffic controllers (ATCs) are giving directions to the artificial pilots and the AOD wants the pilots to respond to the ATC’s with simple responses. The programs that incorporate the speech software must be trained, which means they use neural networks. This is an early stage of the program that has plenty of room for improvement. The improvements are imperative because ATCs use very specific dialogue and the software needs to be able to communicate correctly and promptly every time.
AI-supported Design of Aircraft is used to help designers in the process of creating conceptual designs of aircraft. This program empowers the designers to concentrate more on the design itself and less on the design process. The software also allows the user to focus less on software tools. The AIDA uses rule-based systems to compute its data. This is a diagram of the arrangement of the AIDA modules. Although simple, the program is proving effective.
Haitham Baomar and Peter Bentley are leading a team from the University College of London to develop an artificial intelligence-based Intelligent Autopilot System (IAS) designed to teach an autopilot system to behave like a highly experienced pilot who is faced with an emergency situation such as severe weather, turbulence, or system failure.

Future of Aviation...
One of the more promising innovations is the idea of a personal AI tutor or assistant for each individual student. Because a single teacher can’t work with every student at once, AI tutors would allow for students to get extra, one-on-one help in areas of needed growth. There are many new possibilities due to what has been coined by The New York Times as “The Great AI Awakening.” One of these possibilities mentioned by Forbes included the providing of adaptive learning programs, which assess and react to a student’s emotions and learning preferences.
Many teachers fear the idea of AI replacing them in the classroom, especially with the idea of personal AI assistants for each student. The reality is, AI can create a more dystopian environment with revenge effects. AI technologies will inevitably be taking over the classroom in the years to come, thus the kinks of these new innovations must be worked out before teachers decide whether or not to implement them into their daily schedules.

Preparing Students for the future.
Artificial Intelligence has inspired numerous creative applications including its usage to produce visual art. The recent AI-based exhibitions provide a good overview of the historical applications of AI for art, architecture, and design. These exhibitions showcasing the usage of AI to produce art include the Google-sponsored benefit and auction at the Gray Area Foundation in San Francisco, where artists experimented with the DeepDream algorithm. The Association of Computing Machinery dedicated a special magazine issue to the subject of computers and art highlighting the role of machine learning in the arts.
The 1980s was really when AI started to become prominent in the finance world. This is when expert systems became more of a commercial product in the financial field. Their first function was to help give financial plans for people with incomes over a threshold. That then led to the Client Profiling System that was used for a specific band of incomes. The 1990s was a lot more about fraud detection. One of the systems that were started in 1993 was able to review over lacs of transactions per week and over two years it helped identify 400 potential cases of money laundering which would have been equal to $1 billion. Although expert systems did not last in the finance world, it did help jump-start the use of AI and help make it what it is today.
These days AI is prominent in the following use cases in the financial world:
  • Algorithmic Trading
  • Market Analysis
  • Personal Finance
  • Underwriting
The potential uses of AI in government are wide and varied, with recent research suggesting that ‘Cognitive technologies could eventually revolutionize every facet of government operations’. Experts in government consulting suggest these type of government problems are appropriate for AI applications:
  • Resource allocation
  • Large public & employees datasets
  • Experts shortage
  • Predictable scenarios
  • Procedural tasks
  • Diverse data
The main military applications of Artificial Intelligence and Machine Learning are to enhance C2, Communications, Sensors, Integration, and Interoperability. Artificial Intelligence technologies enable coordination of sensors and effectors, threat detection and identification, marking of enemy positions, target acquisition, coordination, and deconfliction of distributed Join Fires between networked combat vehicles and tanks also inside Manned and Unmanned Teams (MUM-T).

Mini-Unmanned Tank
In video games, artificial intelligence is routinely used to generate dynamic purposeful behavior in non-player characters (NPCs). Besides, well-understood AI techniques are routinely used for pathfinding. Some researchers consider NPC AI in games to be a “solved problem” for most production tasks. Games with more atypical AI include the AI director of Left 4 Dead (2008) and the neuroevolutionary training of platoons in Supreme Commander 2 (2010).
Companies are making computer-generated news and report commercially available, including summarizing team sporting events based on statistical data from the game in English and also financial reports and real estate analyses.
There are software firms that help publishers increase traffic by ‘intelligently’ posting articles on social media platforms such as Facebook and Twitter. Another firm uses AI to turn structured data into intelligent comments and recommendations in natural language such as financial reports, executive summaries, personalized sales or marketing documents.
Yet another firm has launched an app that is designed to learn how to best engage each individual reader with the exact articles — sent through the right channel at the right time — that will be most relevant to the reader. There are firms that are helping media companies with their AI-powered video personalization and programming platform.
It is possible to use AI to predict or generalize the behavior of customers from their digital footprints to target them with personalized promotions or build customer personas automatically. A documented case reports that online gambling companies were using AI to improve customer targeting.
Moreover, the application of Personality computing AI models can help to reduce the cost of advertising campaigns by adding psychological targeting to more traditional sociodemographic or behavioral targeting.
For further details, refer following Wikipedia article:

Disadvantages:

As every bright side has a darker version in it, AI also has some disadvantages. Let’s have a look at some of them:
One concern is that AI programs may be programmed to be biased against certain groups, such as women and minorities because most of the developers are wealthy Caucasian men. Recent researches show that support for artificial intelligence is higher among men than women.
Algorithms have a host of applications in today’s legal system already, assisting officials ranging from judges to parole officers and public defenders in gauging the predicted likelihood of recidivism of defendants. An AI-based criminal offender profiling application assigns an exceptionally elevated risk of recidivism to black defendants while, conversely, ascribing low-risk estimates to white defendants significantly more often than statistically expected.
The relationship between automation and employment has always been complicated. While automation eliminates old jobs, it also creates new jobs through micro-economic and macro-economic effects. Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence. The Economist states that ‘the worry that AI could do to white-collar jobs what steam power did to blue-collar ones during the Industrial Revolution’ is ‘worth taking seriously’. Jobs at extreme risk range from paralegals to fast-food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy. Many futurists warn that these jobs may be automated in the next couple of decades and that many of the new jobs may not be ‘accessible to people with average capability’, even with retraining. Economists point out that in the past technology has tended to increase rather than reduce total employment, but acknowledge that ‘we’re in uncharted territory’ with AI.
Some experts suggest that AI applications cannot, by definition, successfully simulate genuine human empathy and that the use of AI technology in fields such as customer service or psychotherapy was deeply misguided. Few experts are also bothered that AI researchers (and some philosophers) were willing to view the human mind as nothing more than a computer program (a position is now known as computationalism) which implies that AI research devalues human life.
Currently, many countries are researching battlefield robots, including the United States, China, Russia, and the United Kingdom. Many people concerned about risk from superintelligent AI also want to limit the use of artificial soldiers and drones.
Physicist Stephen Hawking, Microsoft founder Bill Gates, and SpaceX founder Elon Musk have expressed concerns about the possibility that AI could evolve to the point that humans could not control it, with Hawking theorizing that this could ‘spell the end of the human race’.

Potential Threat to Humanity, Jobs, etc.
For this danger to be realized, the hypothetical AI would have to overpower or out-think all of humanity, which a minority of experts argue is a possibility far enough in the future to not be worth researching. Other counterarguments revolve around humans being either intrinsically or convergently valuable from the perspective of AI.
For further details, refer ‘Potential harm’ section of following Wikipedia article:
Creating artificial intelligence is perhaps the biggest event for mankind. If used and developed constructively, we can use artificial intelligence to eradicate poverty and hunger from the human race.
The argument that will we ever achieve that supreme level of AI ever is ongoing. The creators and perpetrators of artificial intelligence insist that machine intelligence is beneficial and has been created to help the human race.
The power of artificial intelligence that inadvertently causes destruction and damage cannot be ignored. What will help us control it better is research and in-depth study of the importance of artificial intelligence. Research alone can control the potentially harmful consequences of AI and help us enjoy the fruit of this innovation.
AI will not only change the way we think or live our lives but also explores new horizons, even if its space or the ocean. Humans are getting continually better in defining their desires and quickly transforming this desire into reality. Things will happen so fast that we will not notice the minor changes and will be easily adaptable to the change it brings to us.
Credits: @towardsdatascience

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