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Artificial intelligence, or AI, is a computer system which learns from the experiences it encounters. It can adjust on its own to new inputs, allowing it to perform tasks in a way that is similar to what a human would do. How we have defined AI over the years has changed, as have the tasks weve had these machines complete.

As a term, artificial intelligence was defined in 1956. With increasing levels of data being processed, improved storage capabilities, and the development of advanced algorithms, AI can now mimic human reasoning. AI personal assistants, like Siri or Alexa, have been around for military purposes since 2003.

With these artificial intelligence pros and cons, it is important to think of this technology as a decision support system. It is not the type of AI from science-fiction stories which attempts to rule the world by dominating the human race.

1. Artificial intelligence completes routine tasks with ease.Many of the tasks that we complete every day are repetitive. That repetition helps us to get into a routine and positive work flow. It also takes up a lot of our time. With AI, the repetitive tasks can be automated, finely tuning the equipment to work for extended time periods to complete the work. That allows human workers to focus on the more creative elements of their job responsibilities.

2. Artificial intelligence can work indefinitely.Human workers are typically good for 8-10 hours of production every day. Artificial intelligence can continue operating for an indefinite time period. As long as there is a power resource available to it, and the equipment is properly cared for, AI machines do not experience the same dips in productivity that human workers experience when they get tired at the end of the day.

3. Artificial intelligence makes fewer errors.AI is important within certain fields and industries where accuracy or precision is the top priority. When there are no margins for error, these machines are able to breakdown complicated math constructs into practical actions faster, and with more accuracy, when compared to human workers.

4. Artificial intelligence helps us to explore.There are many places in our universe where it would be unsafe, if not impossible, for humans to see. AI makes it possible for us to learn more about these places, which furthers our species knowledge database. We can explore the deepest parts of the ocean because of AI. We can journey to inhospitable planets because of AI. We can even find new resources to consume because of this technology.

5. Artificial intelligence can be used by anyone.There are multiple ways that the average person can embrace the benefits of AI every day. With smart homes powered by AI, thermostat and energy regulation helps to cut the monthly utility bill. Augmented reality allows consumers to picture items in their own home without purchasing them first. When it is correctly applied, our perception of reality is enhanced, which creates a positive personal experience.

6. Artificial intelligence makes us become more productive.AI creates a new standard for productivity. It will also make each one of us more productive as well. If you are texting someone or using word processing software to write a report and a misspelled word is automatically corrected, then youve just experienced a time benefit because of AI. An artificial intelligence can sift through petabytes of information, which is something the human brain is just not designed to do.

7. Artificial intelligence could make us healthier.Every industry benefits from the presence and use of AI. We can use AI to establish healthier eating habits or to get more exercise. It can be used to diagnose certain diseases or recommends a treatment plan for something already diagnosed. In the future, AI might even assist physicians who are conducting a surgical procedure.

8. Artificial intelligence extends the human experience.With an AI helping each of us, we have the power to do more, be more, and explore more than ever before. In some ways, this evolutionary process could be our destiny. Some believe that computers and humanity are not separate, but instead a single, cognitive unit that already works together for the betterment of all. Through AI, people who are blind can now see. Those who are deaf can now hear. We become better because we have a greater capacity to do thins.

1. Artificial intelligence comes with a steep price tag.A new artificial intelligence is costly to build. Although the price is coming down, individual developments can still be as high as $300,000 for a basic AI. For small businesses operating on tight margins or low initial capital, it may be difficult to find the cash necessary to take advantage of the benefits which AI can bring. For larger companies, the cost of AI may be much higher, depending upon the scope of the project.

2. Artificial intelligence will reduce employment opportunities.There will be jobs gained because of AI. There will also be jobs lost because of it. Any job which features repetitive tasks as part of its duties is at-risk of being replaced by an artificial intelligence in the future. In 2017, Gartner predicted that 500,000 net jobs would be created because of AI. On the other end of the spectrum, up to 900,000 jobs could be lost because of it. Those figures are for jobs only within the United States.

3. Artificial intelligence will be tasked with its own decisions.One of the greatest threats we face with AI is its decision-making mechanism. An AI is only as intelligent and insightful as the individuals responsible for its initial programming. That means there could be a certain bias found within is mechanisms when it is time to make an important decision. In 2014, an active shooter situation caused people to call Uber to escape the area. Instead of recognizing the dangerous situation, the algorithm Uber used saw a spike in demand, so it decided to increase prices.

4. Artificial intelligence lacks creativity.We can program robots to perform creative tasks. Where we stall out in the evolution of AI is creating an intelligence which can be originally creative on its own. Our current AI matches the creativity of its creator. Because there is a lack of creativity, there tends to be a lack of empathy as well. That means the decision of an AI is based on what the best possible analytical solution happens to be, which may not always be the correct decision to make.

5. Artificial intelligence can lack improvement.An artificial intelligence may be able to change how it reacts in certain situations, much like a child stops touching a hot stove after being burned by it. What it does not do is alter its perceptions, responses, or reactions when there is a changing environment. There is an inability to distinguish specific bits of information observed beyond the data generated by that direct observation.

6. Artificial intelligence can be inaccurate.Machine translations have become an important tool in our quest to communicate with one another universally. The only problem with these translations is that they must be reviewed by humans because the words, not the intent of the words, is what machines translate. Without a review by a trained human translator, the information received from a machine translation may be inaccurate or insensitive, creating more problems instead of fewer with our overall communication.

7. Artificial intelligence changes the power structure of societies.Because AI offers the potential to change industries and the way we live in numerous ways, societies experience a power shift when it becomes the dominant force. Those who can create or control this technology are the ones who will be able to steer society toward their personal vision of how people should be. It also removes the humanity out of certain decisions, like the idea of having autonomous AI responsible for warfare without humans actually initiating the act of violence.

8. Artificial intelligence treats humanity as a commodity.When we look at the possible outcomes of AI on todays world, the debate is often about how many people benefit compared to how many people will not. The danger here is that people are treated as a commodity. Businesses are already doing this, looking at the commodity of automation through AI as a better investment than the commodity of human workers. If we begin to perceive ourselves as a commodity only, then AI will too, and the outcome of that decision could be unpredictable.

These artificial intelligence pros and cons show us that our world can benefit from its presence in a variety of ways. There are also many potential dangers which come with this technology. Jobs may be created, but jobs will be lost. Lives could be saved, but lives could also be lost. That is why the technologies behind AI must be made available to everyone. If only a few hold the power of AI, then the world could become a very different place in a short period of time.

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16 Artificial Intelligence Pros and Cons Vittana.org

This is a list of all Artificial Intelligence ETFs traded in the USA which are currently tagged by ETF Database. Please note that the list may not contain newly issued ETFs. If youre looking for a more simplified way to browse and compare ETFs, you may want to visit our ETFdb Categories, which categorize every ETF in a single best fit category.

This page includes historical return information for all Artificial Intelligence ETFs listed on U.S. exchanges that are currently tracked by ETF Database.

The table below includes fund flow data for all U.S. listed Artificial Intelligence ETFs. Total fund flow is the capital inflow into an ETF minus the capital outflow from the ETF for a particular time period.

Fund Flows in millions of U.S. Dollars.

The following table includes expense data and other descriptive information for all Artificial Intelligence ETFs listed on U.S. exchanges that are currently tracked by ETF Database. In addition to expense ratio and issuer information, this table displays platforms that offer commission-free trading for certain ETFs.

Clicking on any of the links in the table below will provide additional descriptive and quantitative information on Artificial Intelligence ETFs.

The following table includes ESG Scores and other descriptive information for all Artificial Intelligence ETFs listed on U.S. exchanges that are currently tracked by ETF Database. Easily browse and evaluate ETFs by visiting our Responsible Investing themes section and find ETFs that map to various environmental, social and governance themes.

This page includes historical dividend information for all Artificial Intelligence listed on U.S. exchanges that are currently tracked by ETF Database. Note that certain ETFs may not make dividend payments, and as such some of the information below may not be meaningful.

The table below includes basic holdings data for all U.S. listed Artificial Intelligence ETFs that are currently tagged by ETF Database. The table below includes the number of holdings for each ETF and the percentage of assets that the top ten assets make up, if applicable. For more detailed holdings information for any ETF, click on the link in the right column.

The following table includes certain tax information for all Artificial Intelligence ETFs listed on U.S. exchanges that are currently tracked by ETF Database, including applicable short-term and long-term capital gains rates and the tax form on which gains or losses in each ETF will be reported.

This page contains certain technical information for all Artificial Intelligence ETFs that are listed on U.S. exchanges and tracked by ETF Database. Note that the table below only includes limited technical indicators; click on the View link in the far right column for each ETF to see an expanded display of the products technicals.

This page provides links to various analyses for all Artificial Intelligence ETFs that are listed on U.S. exchanges and tracked by ETF Database. The links in the table below will guide you to various analytical resources for the relevant ETF, including an X-ray of holdings, official fund fact sheet, or objective analyst report.

This page provides ETFdb Ratings for all Artificial Intelligence ETFs that are listed on U.S. exchanges and tracked by ETF Database. The ETFdb Ratings are transparent, quant-based evaluations of ETFs relative to other products in the same ETFdb.com Category. As such, it should be noted that this page may include ETFs from multiple ETFdb.com Categories.

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Top 45 Artificial Intelligence ETFs - ETFdb.com

AI jobs are on the upswing, as are the capabilities of AI systems. The speed of deployments has also increased exponentially. It's now possible to train an image-processing algorithm in about a minute -- something that took hours just a couple of years ago.

These are among the key metrics of AI tracked in the latest release of theAI Index, an annual data update from Stanford University'sHuman-Centered Artificial Intelligence Institutepublished in partnership with McKinsey Global Institute. The index tracks AI growth across a range of metrics, from papers published to patents granted to employment numbers.

Here are some key measures extracted from the 290-page index:

AI conference attendance: One important metric is conference attendance, for starters. That's way up. Attendance at AI conferences continues to increase significantly. In 2019, the largest, NeurIPS, expects 13,500 attendees, up 41% over 2018 and over 800% relative to 2012. Even conferences such as AAAI and CVPR are seeing annual attendance growth around 30%.

AI jobs: Another key metric is the amount of AI-related jobs opening up. This is also on the upswing, the index shows. Looking at Indeed postings between 2015 and October 2019, the share of AI jobs in the US increased five-fold since 2010, with the fraction of total jobs rising from 0.26% of total jobs posted to 1.32% in October 2019. While this is still a small fraction of total jobs, it's worth mentioning that these are only technology-related positions working directly in AI development, and there are likely an increasingly large share of jobs being enhanced or re-ordered by AI.

Among AI technology positions, the leading category being job postings mentioning "machine learning" (58% of AI jobs), followed by artificial intelligence (24%), deep learning (9%), and natural language processing (8%). Deep learning is the fastest growing job category, growing 12-fold between 2015 and 2018. Artificial Intelligence grew by five-fold, machine learning grew by five-fold, machine learning by four-fold, and natural language processing two-fold.

Compute capacity: Moore's Law has gone into hyperdrive, the AI Index shows, with substantial progress in ramping up the computing capacity required to run AI, the index shows. Prior to 2012, AI results closely tracked Moore's Law, with compute doubling every two years. Post-2012, compute has been doubling every 3.4 months -- a mind-boggling net increase of 300,000x. By contrast, the typical two-year doubling period that characterized Moore's law previously would only yield a 7x increase, the index's authors point out.

Training time: The among of time it takes to train AI algorithms has accelerated dramatically -- it now can happen in almost 1/180th of the time it took just two years ago to train a large image classification system on a cloud infrastructure. Two years ago, it took three hours to train such a system, but by July 2019, that time shrunk to 88 seconds.

Commercial machine translation: One indicator of where AI hits the ground running is machine translation -- for example, English to Chinese. The number of commercially available systems with pre-trained models and public APIs has grown rapidly, the index notes, from eight in 2017 to over 24 in 2019. Increasingly, machine-translation systems provide a full range of customization options: pre-trained generic models, automatic domain adaptation to build models and better engines with their own data, and custom terminology support."

Computer vision: Another benchmark is accuracy of image recognition. The index tracked reporting through ImageNet, a public dataset of more than 14 million images created to address the issue of scarcity of training data in the field of computer vision. In the latest reporting, the accuracy of image recognition by systems has reached about 85%, up from about 62% in 2013.

Natural language processing: AI systems keep getting smarter, to the point they are surpassing low-level human responsiveness through natural language processing. As a result, there are also stronger standards for benchmarking AI implementations. GLUE, the General Language Understanding Evaluation benchmark, was only released in May 2018, intended to measure AI performance for text-processing capabilities. The threshold for submitted systems crossing non-expert human performance was crossed in June, 2019, the index notes. In fact, the performance of AI systems has been so dramatic that industry leaders had to release a higher-level benchmark, SuperGLUE, "so they could test performance after some systems surpassed human performance on GLUE."

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Artificial intelligence jobs on the rise, along with everything else AI - ZDNet

One of the challenges for those tracking the artificial intelligence industry is that, surprisingly, theres no accepted, standard definition of what artificial intelligence really is. AI luminaries all have slightly different definitions of what AI is. Rodney Brooks says that artificial intelligence doesnt mean one thing its a collection of practices and pieces that people put together. Of course, thats not particularly settling for companies that need to understand the breadth of what AI technologies are and how to apply them to their specific needs.

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In general, most people would agree that the fundamental goals of AI are to enable machines to have cognition, perception, and decision-making capabilities that previously only humans or other intelligent creatures have. Max Tegmark simply defines AI as intelligence that is not biological. Simple enough but we dont fully understand what biological intelligence itself means, and so trying to build it artificially is a challenge.

At the most abstract level, AI is machine behavior and functions that mimic the intelligence and behavior of humans. Specifically, this usually refers to what we come to think of as learning, problem solving, understanding and interacting with the real-world environment, and conversations and linguistic communication. However the specifics matter, especially when were trying to apply that intelligence to solve very specific problems businesses, organizations, and individuals have.

Saying AI but meaning something else

There are certainly a subset of those pursuing AI technologies with a goal of solving the ultimate problem: creating artificial general intelligence (AGI) that can handle any problem, situation, and thought process that a human can. AGI is certainly the goal for many in the AI research being done in academic and lab settings as it gets to the heart of answering the basic question of whether intelligence is something only biological entities can have. But the majority of those who are talking about AI in the market today are not talking about AGI or solving these fundamental questions of intelligence. Rather, they are looking at applying very specific subsets of AI to narrow problem areas. This is the classic Broad / Narrow (Strong / Weak) AI discussion.

Since no one has successfully built an AGI solution, it follows that all current AI solutions are narrow. While there certainly are a few narrow AI solutions that aim to solve broader questions of intelligence, the vast majority of narrow AI solutions are not trying to achieve anything greater than the specific problem the technology is being applied to. What we mean to say here is that were not doing narrow AI for the sake of solving a general AI problem, but rather narrow AI for the sake of narrow AI. Its not going to get any broader for those particular organizations. In fact, it should be said that many enterprises dont really care much about AGI, and the goal of AI for those organizations is not AGI.

If thats the case, then it seems that the industrys perception of what AI is and where it is heading differs from what many in research or academia think. What interests enterprises most about AI is not that its solving questions of general intelligence, but rather that there are specific things that humans have been doing in the organization that they would now like machines to do. The range of those tasks differs depending on the organization and the sort of problems they are trying to solve. If this is the case, then why bother with an ill-defined term in which the original definition and goals are diverging rapidly from what is actually being put into practice?

What are cognitive technologies?

Perhaps a better term for narrow AI being applied for the sole sake of those narrow applications is cognitive technology. Rather than trying to build an artificial intelligence, enterprises are leveraging cognitive technologies to automate and enable a wide range of problem areas that require some aspect of cognition. Generally, you can group these aspects of cognition into three P categories, borrowed from the autonomous vehicles industry:

From this perspective, its clear that while cognitive technologies are indeed a subset of Artificial Intelligence technologies, with the main difference being that AI can be applied both towards the goals of AGI as well as narrowly-focused AI applications. On the other-hand, using the term cognitive technology instead of AI is an acceptance of the fact that the technology being applied borrows from AI capabilities but doesnt have ambitions of being anything other than technology applied to a narrow, specific task.

Surviving the next AI winter

The mood in the AI industry is noticeably shifting. Marketing hype, venture capital dollars, and government interest is all helping to push demand for AI skills and technology to its limits. We are still very far away from the end vision of AGI. Companies are quickly realizing the limits of AI technology and we risk industry backlash as enterprises push back on what is being overpromised and under delivered, just as we experienced in the first AI Winter. The big concern is that interest will cool too much and AI investment and research will again slow, leading to another AI Winter. However, perhaps the issue never has been with the term Artificial Intelligence. AI has always been a lofty goal upon which to set the sights of academic research and interest, much like building settlements on Mars or interstellar travel. However, just as the Space Race has resulted in technologies with broad adoption today, so too will the AI Quest result in cognitive technologies with broad adoption, even if we never achieve the goals of AGI.

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Why Cognitive Technology May Be A Better Term Than Artificial Intelligence - Forbes

Artificial Intelligence (AI) has always been a topic of discussion- is it good enough for us? Getting more and more into this high technology world will give us a better future or not? According to recent research, almost everyone has a different requirement for automation. And most of the work of humans is done by the latest high intelligence computers. You all must be familiar with the fact of how Artificial Intelligence is changing industries, like Medicine, Automobiles, and Manufacturing. Well, what about Architecture?

The main issue is about the fact that these high tech robots will actually replace the creator? Although these high tech computers are not good enough at some ideas and you have to rely on Human Intelligence for that. However, these can be used to save a lot of time by doing some time-consuming tasks, and we can utilize that time in creating some other designs.

Artificial Intelligence is a high technology mechanical system that can perform any task but needs a few human efforts like visual interpretation or design-making etc. AI works and gives the best results possible by analyzing tons of data, and thats how it can excel in architecture.

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While creating new designs, architects usually go through past designs and the data prepared throughout the making of the building. Instead of investing a lot of time and energy to create something new, it is alleged that a computer will be able to analyze the data in a short time period and will give recommendations accordingly. With this, an architect will be able to do testing and research simultaneously and sometimes even without pen and paper. It seems like it will lead to the organizations or the clients to revert to computers for masterplans and construction.

However, the value of architects and human efforts of analyzing a problem and finding the perfect solutions will always remain unchallenged.

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Parametric architecture is a hidden weapon that allows an architect to change specific parameters to create various types of output designs and create such structures that would not have been imagined earlier. It is like an architects programming language.

It allows an architect to consider a building and reframe it to fit into some other requirements. A process like this allows Artificial Intelligence to reduce the effort of an architect so that the architect can freely think about different ideas and create something new.

Constructing a building is not a one-day task as it needs a lot of pre-planning. However, this pre-planning is not enough sometimes, and you need a little bit of more effort to get an architects opinion to life. Artificial Intelligence will make an architects work significantly easier by analyzing the whole data and creating models that can save a lot of time and energy of the architect.

All in all, AI can be called an estimation tool for various aspects while constructing a building. However, when it comes to the construction part, AI can help so that human efforts become negligible.

The countless hours of research at the starting of any new project is where AI steps in and makes it easy for the architect by analyzing the aggregate data in millisecond and recommending some models so that the architect can think about the conceptual design without even using the pen or paper.

Just like while building a home for a family, if you have the whole information about the requirements of the family, you can simply pull all zoning data using AI and generate design variations in a short time period.

This era of modernization demands everything to be smartly designed. Just like smart cities, todays high technology society demands smart homes. However, now architects do not have to bother about how to use AI to create the designs of home only, but they should worry about making the users experience worth paying.

Change is something that should never change. The way your city looks today will be very different in the coming time. The most challenging task for an architect is city planning that needs a lot of precision planning. However, the primary task is to analyze all the possible aspects, and understand how a city will flow, how the population is going to be in the coming years.

All these factors are indicating one thing only, i.e., the future architects will give fewer efforts in the business of drawing and more into satisfying all the requirements of the user with the help of Artificial Intelligence.

Read More: How AI and Automation Are Joining Forces to Transform ITSM

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How is Artificial Intelligence (AI) Changing the Future of Architecture? - AiThority