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Problems caused by AI, atsisiųsti Sorošiuks, gavau trečią dozę, nes…

- - - - McKinsey argues that AI and automation may on one hand facilitate the rise of massively scaled organisations, and on the other will enable small players and even individuals to undertake project work that is now mostly performed by bigger companies.
        
        'Early adopters', that is, companies that fully absorb AI tools over the next five to seven years, will most probably benefit disproportionately. At the other end of the spectrum would be the slow adopters or non-adopters, which are likely to experience some economic decline. The market share is likely to shift from the laggards to the front-runners, which would be able to gradually attract more and more of the profit pool of their industry. This would lead to a 'winnertakes all' phenomenon, similar to what is currently observed on tech markets.
    - - Technologies underpinning this process – such as IoT, 5G, cloud computing, big data analytics, smart sensors, augmented reality, 3D printing and robotics – are likely to transform manufacturing into a single cyber-physical system in which digital technology, internet and production are merged in one.
        
        In the smart factories of the future, production processes would be connected and AI solutions would be fundamental in linking the machines, interfaces, and components (using, for example, visual recognition). Large amounts of data would be collected and fed into AI appliances, which would in turn optimise the manufacturing process.
        
        The OECD reckons this use of AI can be 'applied to most
        industrial activities from optimising multi-machine systems to enhancing industrial research'.
        
        The vast application of AI systems would mean the loss of low paying jobs loss. EKONOMIKAI ČIA STRUCTUAL UNEMPLOYMENT BUS.
        
        Deployment of AI in production is likely to increase over time, due to the development of automated learning processes. Fundamentally, it is likely to boost the competitiveness of the manufacturing sector through efficiency and productivity gains enabled by data analysis, and supply chains would be based on these gains. AI would also boost automation, ensure stronger quality control of products and processes, and preventive diagnostics of machinery status, while also ensuring timely maintenance, near-zero downtime, fewer errors and defective products.
        
        This means that manufacturing processes would be heavily reliant on AI. This would mean that AI would be responsible for drug production or other necessities. For example, if the AI strives for optimisation, it can cut corners and thus, for example, change the composition of drugs. It can also, on its own, take decisions without the persons in charge notice.
    - - Have you ever considered that algorithms could bring down our entire financial system? That’s right, Wall Street. You might want to take notice. Algorithmic trading could be responsible for our next major financial crisis in the markets.
        
        What is algorithmic trading? This type of trading occurs when a computer, unencumbered by the instincts or emotions that could cloud a human’s judgement, execute trades based off of pre-programmed instructions. These computers can make extremely high-volume, high-frequency and high-value trades that can lead to big losses and extreme market volatility. Algorithmic High-Frequency Trading (HFT) is proving to be a huge risk factor in our markets. HFT is essentially when a computer places thousands of trades at blistering speeds with the goal of selling a few seconds later for small profits. Thousands of these trades every second can equal a pretty hefty chunk of change. The issue with HFT is that it doesn’t take into account how interconnected the markets are or the fact that human emotion and logic still play a massive role in our markets.
        
        A sell-off of millions of shares in the airline market could potentially scare humans into selling off their shares in the hotel industry, which in turn could snowball people into selling off their shares in other travel-related companies, which could then affect logistics companies, food supply companies, etc.
        
        Take the “Flash Crash” of May 2010 as an example. Towards the end of the trading day, the Dow Jones plunged 1,000 points (more than $1 trillion in value) before rebounding towards normal levels just 36 minutes later. What caused this crash? A London-based trader named Navinder Singh Sarao first caused the crash and then it became exacerbated by HFT computers. Apparently Sarao used a “spoofing” algorithm that placed an order for thousands of stock index futures contracts betting that the market would fall. Instead of going through with the bet, Sarao was going to cancel the order at the last second and buy the lower priced stocks that were being sold off due to his original bet. Other humans and HFT computers saw this $200 million bet and took it as a sign that the market was going to tank. In turn, HFT computers began one of the biggest stock sell-offs in history, causing a brief loss of more than $1 trillion globally.
- - - - The AI might not be accessible by everyone, since it is costly
      - AI might and will not be able to develop a students emotions, emotional inteligence.
    - - Although the students get their answers fast they may be fixed, thus they may not answer the students exact answer.
      - In the case of a malfunction the answers may even come even slower, since the AI might need a fix.
    - - Problems with personalization using AI
        
        Although the AI might be able to personalize the learning experience, it might not account for a students emotions, wants and needs.
        
        The AI in order to create a personalized learning experience for the student, will have to access the students private information. Thus in the case of a cyber attack the private information will become accessible.
        
        The AI has access to private information, thus it might breach some private information issues
- - - - The deluge has many scientists turning to artificial intelligence for help. With minimal human input, AI systems such as artificial neural networks — computer-simulated networks of neurons that mimic the function of brains — can plow through mountains of data, highlighting anomalies and detecting patterns that humans could never have spotted.
        
        Artificial Intelligence is helping the scientific community in academic publishing as well. It can assist in peer reviews, searching and extracting published content as well as in detecting plagiarism and spotting data fabrication as mentioned in “Artificial Intelligence in Research and Publishing”. AI powered tools are also useful in scientific communication as they are not prone to the same bias as humans.
        
        Do I even need to mention how dangerous it is to let only just AI peer-review scientific articles?
        
        Apart from academia, Artificial Intelligence is also making an impact on scientific journalism. Bertrand Pecquerie, CEO of the Global Editors Network, says that “AI will be the catalyst of the third disruption in journalism, potentially changing the way we produce and consume news.” In today’s world “computers can tell stories without humans. News-writing bots like the LA Times’ Quakebot or Washington Post’s Heliograph are able to come up with larger amounts of news stories than humans and at a much quicker pace. They are likely to sweep into newsrooms and take over much of the media work — in the next few years.
        
        Nowadays, we are dependent on news portals as New York Times, CNN, BBC. We might not even know that AI produces the article. It is perilous in the sense that if the AI independently writes articles without supervision, it can cherry-pick topics and what side of the story to highlight.
        
        A fascinating fact is that these bots can even mimic the voice of your famous writer while writing science papers and articles. Furthermore these bots are equipped with the capability that reporters and editors take decades to develop, i.e. “predict the most important research papers to report on, and the parts of those papers that should be focused on to find newsworthy issues.” AI can overcome the challenges and problems often faced by science writers with limited experience with the task.
        
        To cherry-pick articles is the same as limiting and censoring researchers' or average readers' knowledge. The AI will alter the knowledge that we can get, as it will pick what we need to read.
        
        Giovanni Colavizza, a data scientist doing research at the Alan Turing Institute in London on full-text analysis of scholarly publications, wrote in the International Journal of Science that modern AI tools are equipped with “state-of-the-art information retrieving” capabilities. The article states that a plethora of scientific literature is available on the internet with 1 million new research papers being published every year. Given such an incredible publication pace, it’s almost impossible for scientists to sort through, analyze and assess vast quantities of research papers to test different hypothesis. This problem can be solved with cutting edge tech tools powered by Artificial Intelligence that can help scientists extract specific content as per requirement as they possess the ability to filter, rank and group search results. An example of such a technology is Iris.ai, which acts as a research assistant to help users map out and acquire relevant scientific knowledge.
        
        AI powered tools such as Iris possesses such incredible storage and processing capacity that “she can read the transcripts of all TED talks to date in no time” and here is “how exactly Iris.ai works.” In an article “5 ways Artificial Intelligence will Disrupt Science” ai powered tools such as Iris are specially designed with the astounding capability “to map out the science around a TED talk” as it can “analyze(s) the scripts of the talks. Using Natural Language Processing algorithms, such tools can mine(…) open-access academic literature to find key papers related to the talk’s content” and elegantly visualize the groups of related research papers. For research scientists this means entering a 300–500 word description of their research topic or just the url of an existing paper for Iris to produce a map of 1000’s of matching documents as stated in “How AI Technology Can Tame the Scientific Literature.”
        
        Iris’s cofounder Mario Ritola mentioned that their team’s future aim is to turn Iris from a research assistant to a real scientist. This means that it can generate a hypothesis by itself after analyzing and going through existing scientific papers, collect data by running experiments and simulations and write new papers based on the outcome. She also mentions about “democratizing access to scientific knowledge” and making it publicly available through the use of AI assistants that can map out relevant information by “leveraging AI.”
        
        In fact a team at IBM has already achieved what Ms. Ritola is envisioning. They state that they have developed AI algorithms capable of making new scientific discoveries and works by bringing together text mining, visualization and analytics in order to extract facts and propose new hypotheses that are likely to be true. What this might mean is that in the near future, scientific research might get automated freeing up scientists to focus on more important tasks.
- - - - The accuracy and precision of today’s weapons are steadily forcing contemporary battlefields to empty of human combatants.
        As more and more sensors fill the battlespace, sending vast amounts of data back to analysts, humans struggle to make sense of the mountain of information gathered.
        This is where artificial intelligence (AI) comes in – learning algorithms that thrive off big data; in fact, the more data these systems analyse, the more accurate they can be.
        
        While the much-hyped idea of “Killer Robots” – remorseless machines hunting down humans and “terminating” them for some reason known to themselves – has caught the public’s imagination, the current focus of AI could not be further from that.
        
        Difficulties AI face in the military
        
        Data acquisition and storage
        
        One of the biggest Artificial Intelligence problems is data acquisition and storage. Business AI systems depend on sensor data as its input. For validation of AI, a mountain of sensor data is collected. Irrelevant and noisy datasets may cause obstruction as they are hard to store and analyze.AI works best when it has a good amount of quality data available to it. The algorithm becomes strong and performs well as the relevant data grows. The AI system fails badly when enough quality data isn’t fed into it.
        
        Issue of responsibility
        
        The implementation of AI application comes with great responsibility. Any specific individual must bear the burden of any sort of hardware malfunctions. Earlier, it was relatively easy to determine whether an incident was the result of the actions of a user, developer or manufacturer.
        
        The price factor
        
        Even military struggles a lot when it comes to adopting AI technologies as it is a costly affair.
        
        Lack of computation speed
        
        AI, Machine learning and deep learning solutions require a high degree of computation speeds offered only by high-end processors. Larger infrastructure requirements and pricing associated with these processors has become a hindrance in their general adoption of the AI technology. In this scenario, cloud computing environment and multiple processors running in parallel offer a potent alternative to cater to these computational requirements. As the volume of data available for processing grows exponentially, the computation speed requirements will grow with it. It is imperative to develop next-gen computational infrastructure solutions.
        
        Lack of technical knowledge
        
        To integrate, deploy and implement AI applications in the military the organization must have the knowledge of the current AI advancement and technologies as well as its shortcomings. The lack of technical know-how is hindering the adoption of this niche domain in most of the organizations.