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The Difference Between Artificial Intelligence and Machine Learning

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- AI stands for Artificial Intelligence
- AI normally refers to technology
- AI helps people out with their needs
- AI is man-made
- AI is seen mainly in futuristic, dystopian films and series
- AI is also seen in high-tech countries for customer services (e.g. Japan)
- AI can show incredible knowledge
- Ai can help many people with their jobs and businesses (e.g. scientists, restaurants, airports, hotels, hospitals)

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What does a motivated environment look like? Is this different to some that we have now?

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What are we currently getting wrong to need AI to help us?

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How do we collect data that shows motivation and efficiency in the workplace?

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How much has AI already helped people today?

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Positives and Negatives of Artificial Intelligence

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How have other countries used AI?

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Who and how much would people need AI?

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https://www.youtube.com/watch?v=E8Ox6H64yu8

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Meet Sophia: The first robot declared a citizen by Saudi Arabia

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Take notes on interesting things that you see or hear in both videos.

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- AI means the science of training machine to help with human tasks.
- AI was invented in 1950.
- Machines or robots given human-like properties.
- Machine aren't exactly programmed by people, you give them an example and then they learn of that example.
machine learning need data in order to process information and learn
- Difference is that AI is invented to mimic human abilities, whereas Machine learning is using data to learn, detect, and discover new things.

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Locate a resource (article, video, artefact, artwork, source) that can assist you in successfully answering the question above.

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- Sophia is a humanised robot
- Sophia has the mind of a human
- Sophia has Artificial Intelligence
- Sophia is able to pick up on jokes and living that almost all AI can't do (e.g. siri)
- First robot to be declared citizenship

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Locate a resource (article, video, artefact, artwork, source) that can assist you in successfully answering the question above.

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Example

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The idea of artificial intelligence (AI) - systems so advanced they can mimic or outperform human cognition - first came to prominence in 1950, when British computer scientist Alan Turing proposed an 'imitation game' to assess whether a computer could fool humans into thinking they were communicating with another human. Soon after, researchers at Princeton University in New Jersey built MADALINE, the first artificial neural network applied to a real-world problem. Their system, modelled on the brain and nervous system, learnt to solve a maze through trial-and-error.

Since then, the rise of AI has been enabled by exponentially faster and more powerful computers and large, complex data sets. Applications such as machine learning, whereby a system identifies patterns in large sets of data, have demonstrated the potential for AI to be practical and profitable.

Today, AI forms the basis of computer systems handling tasks such as voice recognition and translation on smartphones, piloting driverless cars, and controlling robots that automate chores in homes and factories. In research, AI is being used in a growing number of applications, such as processing the enormous amounts of data that underpin fields including astronomy and genomics, producing climate models and weather forecasts, and identifying signs of disease in medical imaging.

"AI is a foundational science in the same sense that physics is a foundational science," says Amir Husain, a computer scientist who founded SparkCognition, a company in Austin, Texas, that creates AI-based analytic and security systems. "A lot of people think that AI is a product or technology, but it's actually an enabler for almost everything we do."

Business, as a result, is booming. The 2019 AI Index Report, published by the Stanford Institute for Human-Centered Artificial Intelligence in California, estimates that global private investment in AI in 2019 was more than US$70 billion. The US, China and Europe took the largest share; Israel, Singapore and Iceland were found to invest heavily in per capita terms. Start-ups founded on AI technologies are a major part of the ecosystem, garnering more than $37 billion globally in investments in 2019, up from $1.3 billion raised in 2010, according to the report.

Revenues have also skyrocketed. The International Data Corporation (IDC), a market research company based in Framingham, Massachusetts, predicts that worldwide revenues for the AI market will total $156.5 billion in 2020, an increase of 12.3% over 2019. Although growth in 2020 is slower than in previous years due to the economic impact of the COVID-19 pandemic, the IDC expects that global revenues will surpass $300 billion in 2024.

As nations vie for leadership, AI research output is increasing rapidly. According to our analysis of journal publications and conference papers tracked by the Dimensions database, the global output for AI research grew from just over 52,000 globally in 2000 to roughly 403,000 in 2019, representing an increase of more than 600%. Now the most popular specialization among computer-science PhD students in North America, AI is set to continue its steep, upward trajectory.

Rising revenues

The United States has historically been the leader in AI-related research output, having accumulated the highest number of publications over the past two decades. But China has ramped up its output in recent years. In each year from 2016 to 2019, China produced more AI-related papers than any other nation, according to Dimensions. Over this period, China's output of AI-related research increased by just over 120%, whereas output in the US increased by almost 70%. In 2019, China published 102,161 AI-related papers, and the US published 74,386. India, which came in third, published 23,398.

Publication numbers aren't the whole story, says Jeffrey Ding, a PhD student at the Future of Humanity Institute at the University of Oxford, UK, who studies China's AI strategy. In the AI Index Report, which uses citation numbers to measure the quality of AI papers, papers from China were cited about 20% less than the world average in 2019, whereas papers from the US were cited about 40% more than average. "Just pumping out raw numbers of papers that don't have a lasting impact isn't really useful," says Ding. "It's more important to keep up with the technology frontier."

A Nature Index analysis for this supplement looked at the number of AI-related articles published in the 82 high-quality natural-science journals tracked by the index, which primarily concern the application of AI to research in the broad fields of chemistry, the physical sciences, life sciences, and Earth and environmental sciences. Between 2015 and 2019, the US was the leader, with the UK, Germany and China in second, third and fourth place, respectively. But China has increased its output in journals tracked by the index. Although it was the fourth-most prolific country in the index in 2015, with roughly half as many AI-related papers as Germany, China crept up over the next three years, then leapt to second place in 2019, showing an increase of 340%. The US, UK and Germany slightly more than doubled their output over the same period.

For the near future, Ding says, the US is likely to remain the world leader in AI. "Though China has some exceptional universities, such as Tsinghua University, the US dominates in terms of maybe the top 20 universities doing AI research, and that is reflected in the quality of the papers. It's very unlikely that China will become the singular innovation centre by 2030."

Many countries see AI as providing a competitive edge, not only economically, but militarily, says Husain. He likens the competition in AI to the Space Race of the mid-twentieth century, in which the US and the Soviet Union vied to be the first to achieve milestones in space travel. "The Space Race yielded contributions that differentiated the American technological ecosystem from all others for decades to come," says Husain. "If a country invests heavily in this area, it will yield technologies that will form the pillar of defence capability and economic differentiation for the rest of the century."

Technologies that can be developed based on AI will indeed have both economic and military benefit, says Daniel Araya, a policy analyst at the Center for International Governance Innovation, a think tank in Ontario, Canada. "We're talking new weapons, data-driven innovation for industry and automation, and redesigning how our society works from the ground up."

Husain points to Germany, which maintains a strong economy that relies on exports of products such as machine parts and automobiles, even though lower-income countries can provide low-wage labour for manufacturing. Germany has been able to compete by using automation to keep manufacturing costs down, while keeping quality and productivity high. AI could reinforce this advantage by powering the next generation of automation technologies. "Anybody that has mastery over this technology and is investing in implementing it retains an economic lead," says Husain. Institutions in Germany, such as the Fraunhofer Society, Europe's largest application-oriented research organization, have been emphasizing Industry 4.0, a national strategic initiative from the German government to introduce more digital innovation and advanced robotics into manufacturing and supply-chain management.

In China, the ability offered by AI systems to monitor public spaces and scan Internet traffic in an effort to glean user intentions may provide the state with improved tools for social control, enhancing its capability for monitoring the population or censoring information. Even in countries that don't officially track their populaces, facial-recognition technology, such as that produced by New York-based company, Clearview AI, is being used by law enforcement to identify suspects. The technology has been met with deep concern by some researchers, who say that biases built into its algorithms could result in ethical and human rights abuses.

Amid the controversy that surrounds certain applications of AI, some groups are highlighting the good it can do. In 2019, the Association for the Advancement of Artificial Intelligence, a scientific society in Menlo Park, California, launched its Artificial Intelligence for the Benefit of Humanity award, a US$1-million prize funded by Squirrel AI, an education technology company based in Shanghai, China. The inaugural winner, Regina Barzilay from the Massachusetts Institute of Technology (MIT) in Cambridge, Massachusetts, received the award in September 2020 for developing a machine-learning algorithm that can examine mammograms and predict which women are at a higher risk of breast cancer. Barzilay has also developed a pattern-recognition algorithm that predicts which molecules might make good candidates for new medications. Publishing in the journal Cell, Barzilay and her colleagues described how their system identified a molecule, dubbed halicin, as a potentially potent new antibiotic ( J. M. Stokes et al. Cell 180, 688-702; 2020). When the molecule was synthesized and tested, it was found to kill antibiotic-resistant bacteria. Barzilay continues to work on halicin and hopes to progress it to clinical trials.

Money to spend

With an eye to the potential benefits of AI-based technologies, the US National Science Foundation (NSF) announced in August 2020 that it is establishing five new institutes focused on different topics, each led by a different university, and each to receive $20 million over five years. One, led by the University of Oklahoma in Norman, will use AI systems to improve climate forecasting accuracy. Another, at the University of Texas at Austin, will focus on the next generation of machine-learning algorithms. A third, led by the University of Colorado Boulder, will apply AI technologies to teaching and learning. The fourth, headed by the University of Illinois at Urbana-Champaign, will explore the discovery and synthesis of new materials and drugs using AI systems. And a fifth, led by MIT, will investigate how AI can improve research in fundamental physics. The NSF has put out a call for proposals for eight more AI institutes, which it plans to announce next year. "We have a long history of supporting basic research in artificial intelligence," says Erwin Gianchandani, the NSF's deputy assistant director for computer and information science and engineering.

In addition, the US Department of Agriculture's National Institute of Food and Agriculture has committed to funding another two institutes each with $20 million over five years to apply AI to questions of crop yield, pest resistance and food distribution.

Access to massive data sets on which to train machine-learning systems is one advantage that both the US and China have, says Araya. Europe, on the other hand, has stringent data laws, which protect people's privacy, but limit its resources for training AI algorithms. "So, it seems unlikely that Europe will produce very sophisticated AI as a consequence," he says. "China is at the opposite end of the spectrum, where it has few data protections, and huge access to varied and diverse pieces of data."

The NSF doesn't make decisions based on global competition, says Rebecca Keiser, the organization's first chief of research security strategy and policy. Her position is designed to safeguard the security of federally funded research, while promoting international collaborations. "We think of it more in terms of the impact that our funding is going to have, rather than from a nation perspective. The fact that it also contributes to US global science leadership is fabulous," says Keiser. As for competing against China in AI, she says, "We don't really know how much, and where, they're investing, so it's hard to compete against that."

With funding from other major US agencies, including the Defense Advanced Research Projects Agency and National Institutes of Health, the US government expected to spend almost $5 billion on unclassified AI research in 2020. Other countries are investing, too. The UK's Engineering and Physical Sciences Research Council gave out nearly £160 million (US$212 million) in 2020. The European Commission, which has been supporting research through its Horizon 2020 programme, says public and private investment in AI should reach €20 billion (US$23 billion) across Europe by the end of 2020. Even with other countries placing emphasis on AI research, including Israel, Japan, Singapore and Australia, the race comes down to China versus the US, Araya says.

The refinement of AI systems is driven not simply by academic questions, but by applying the systems' intelligence to practical problems. Countries will probably make advances by using AI in areas where they already have expertise, says Ding, such as automation in Germany or robotics in Japan. Having so many researchers around the globe tackling all aspects of AI should advance the whole field, says Keiser. "It's always good to have a bit of healthy competition."

Nature 588, S102-S104 (2020)
doi: https://doi.org/10.1038/d41586-020-03409-8

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The race to the top among the world's leaders in artificial intelligence

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As revenues and research output soar in the field of AI, global competition between the United States, China and Europe heats up. As revenues and research output soar in the field of AI, global competition between the United States, China and Europe heats up.

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This website clearly shows examples of AI used today and what they can already do for us in our daily lives. An example is a self-driving car: Tesla.

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28 Examples of Artificial Intelligence Shaking Up Business as Usual

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These artificial intelligence examples are transforming a number of industries, including robotics, healthcare, transportation, finance and more.

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This is what makes employees happy at work | The Way We Work, a TED series

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Advantages and Disadvantages of Artificial Intelligence

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Artificial Intelligence is one of the emerging technologies which tries to simulate human reasoning in AI systems. John McCarthy invented…

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3 Billion working people and 40% identify as happy. Meaning 60% are not happy. It's all about how people work together and are happy with their leaders. There needs to be a level of respect and trust. Using Artificial Intelligence might be able to inform employees about growth mindset and continuous reflection on how people feel at work.

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Write a short descriptive passage to explain how AI would be used in the future.

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https://www.jigsawacademy.com/5-industries-which-rely-heavily-on-artificial-intelligence-and-machine-learning/

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How To Integrate AI Into Your Web Design Workflow

INTRODUCTION Technology has been developing at an exponential rate that exceeds many individuals' wildest expectations. From self-driving cars to 3D printing in the medical field, everyone is waiting to see what's coming next, including web designers. Advances in the tech industry shape and influence the role of the web design profession. In order to stay in the game, designers need to be on top of every new update in the business to stay relevant, and this update is sure to be a gamechanger: artificial intelligence. Unlike other machines, AI learns from its mistakes and is constantly learning which correlations make sense and how to complete a task in the most efficient manner, making better choices each time. With many people fearing the day when a robot will take their job, web designers too wonder if they need to start looking for another line of work anytime soon. Before you start sending out your résumé, though, find out how AI can work for you instead of the other way around. In this article let us look at: Collaborative creationIntelligent design and productionResponsive testingLaunching into the future 1. Collaborative creation The best ideas are born when a group of diverse minds work together. However, web design is more of an independent job with inputs coming mostly from a client who may or may not be educated in design. This is where AI comes in handy. Machine learning programs such as Wix ADI and Grid help with the collaboration of a website's design by asking you a series of questions covering the type of business and preferred kind of site. Your responses are then run through an algorithm to best determine setup and design. Here's a walkthrough on how to get started with Wix ADI: Step 1 After creating an account, you will have the option to create a new site. When you click that option, it will take you to this screen: Click on the option that best describes the kind of site you want to make. For this example, I went with "Blog." Step 2 Once you decide which site to make, you'll then be taken to this window: Click the "Start Now" button on the left to utilize Wix's AI functions. Step 3 You'll then be asked for more specifics on what your website is about. For this example, I chose a website for an artist. Step 4 Once I selected "Artist," Wix ADI asked more specifically what kind of artist I am. Depending on your input, it will ask for more information to leave less room for error when it makes the site. I just left it as "Artist Blog" and clicked "Next." Step 5 Wix ADI will then give you the option to add several features to your site. For this example, I selected all three. Step 6 Next, you'll name your website. Step 7 You'll then be asked the whereabouts of your business. This is optional, so I clicked "Skip." Step 8 Afterwards, you'll be prompted to add more client information. Step 9 Click "Start." Step 10 Choose a color scheme and design for your site. I went with "Skyscraper" in this example. Step 11 There you have it! You're all done creating your site. Step 12 If there's anything you or your client don't like about the site, you can manipulate and change features by clicking the button on the bottom right corner. These tools are constantly learning, combing through the web for relevant information to design a site that is similar to other websites in the field of business that was previously depicted. However, this isn't an end-all be-all solution. No matter how advanced machines and AI become, there's no such thing as perfection. The algorithm might not come up with a site that's 100 percent to your liking. For example, you may not like where the navigation bar is placed or how certain pages are laid out. You may want different images than what was offered to you by the AI. The value of machine learning in this process is to help move designers more quickly and efficiently through this part of the workflow. Since the AI is looking at the data of sites that are similar in nature to yours, it should help you get on the right track from the beginning. The concern with using AI in this part of the process, though, is that it won't generate a site to your or your client's liking every time. Different features and colors may clash together and not deliver what you thought it had promised. It could also stunt idea growth and innovation since you may want to stop at the first website the program generates for you and not look into more creative routes and options. 2. Intelligent design and production The final design and coding of the website are two of the most important steps in the web design workflow. Your sitemap and wireframe give you a pretty good idea of how to structure your client's site and the coding will make that site functional. However, aesthetics are just as important to user experience as layout and usability. Although you might not have thought this before, a color scheme is one of the more crucial decisions you can make in the web design process. According to one study, it takes under 2 minutes for people to form an opinion on whether or not they want to buy a product, and between 60 to 90 percent of that decision is based just on colors. If that statistic doesn't make you think twice about your color choices, it should. However, with thousands of palettes to choose from, picking the right one for your client's website can be difficult. However, AI can also help in this area with the program Colormind. Colormind is a mix of AI and machine learning that generates color palettes. This program uses algorithms to scan images of photography, movies, and art to create successful color schemes. Here it is in action: When you visit the site and click the "Website Colors" page, this is what you'll see: Once you scroll down, you'll see how the above colors are implemented into a dummy site. You can easily change the color palette to see how another color would interact with some of the ones that have already been selected. For example, if you would like to see how a brown color would look instead of the green, click the green color swatch. Then move the color picker to a brown color. Once you select the color you want, scroll back down to see how it looks. If you would like a completely different color palette to experiment with, click the generate button. Although many web designers already look at these different sources of media for inspiration, AI can pick out pleasing palettes in less than a second. You can also input your own hues and Colormind will use what it has learned to come up with a set of other matching colors. The learning aspect of Colormind gives you peace of mind that the color palettes that are being generated are sampled from media that has seen success in their respective fields. You know the colors work together since they have worked for others too. The downside of using this AI is that you can run into the same problem of not being able to choose a color scheme because there are limitless options to choose from. You may not find any you like as well. Also, since these colors are sampled from images online, you may have the issue of selecting a palette that has been overused. That being said, choosing which palette to use becomes infinitely easier when you know the kind of mood you're trying to evoke with your website. Depending on the type of business your client is involved in, some emotions will be more fitting than others. Colors can help online visitors get on the right track, but if the content doesn't match the emotion your client is trying to get across, the success of your website could be at stake. That's where Watsonfinds comes in handy. Sometimes as a web designer, you get to put your content writing hat on when clients have little or no content to offer for the site you're creating. When writing content, you have to keep in mind the emotions you're trying to convey, which can get tricky. Instead of guessing or trying to get multiple opinions on it, have Watsonfinds tell you the mood you're setting. For example, when I copied and pasted this article on positive thinking: These were the results I received: On the other hand, when I copied and pasted this article on coping with the loss of a loved one: These were the results I got: This AI software analyzes sentences and deduces what language is being used, the context of what's being written, and the culture that is being targeted to predict emotional responses from viewers. This makes the learning aspect of this AI very valuable especially if you're trying to reach an international audience. However, this program isn't 100 percent accurate every time. When I inserted a horror-themed short story, the main emotion Watsonfinds derived was joy. After reading that short story, the last thing I was feeling was joy. That being said, take the results you receive with a grain of salt. Some tweaking may need to be in order to get the desired emotion you want, but know that this AI can make mistakes too. When you have the design and content of your site where it needs to be, it's coding time. Coding a site from scratch takes a lot of work and can be more or less difficult depending on your skill level. However, AI can help again with an app that uses artificial intelligence to turn design mockups into source code. When given an image, this app's neural network predicts what you're trying to achieve and generates the code for you. With a success rate of over 70 percent, it's a good software to have on hand, but it goes without saying that coding skills are still a must when using this tool. 3. Responsive testing When your website is set up and done, there's probably going to be a bug or two that'll need fixing. Finding every bug, even with a team, is practically impossible, especially as the site morphs and changes with additional pages or buttons. Software testing AI can really come in handy with this step in the web design workflow. Bugs and failures (such as dead links and unusual pricing) will be searched throughout your site with a report and screenshots sent to you when it's done. Another bonus to using machine learning tools is that there's no need to rewrite test scripts when a change is made to the site. The algorithm runs without the use of scripts, learning on the go what's new and how to interact with unfamiliar data, saving you much-needed time. However, there are some setbacks by leaving testing in the hands of AI. As this technology learns what correlates and what doesn't, it's going to make correlations that don't make sense. Without the judgement call of a human, AI can potentially relate things that make sense arbitrarily (such as if pen prices are rising and global temperatures are rising, then the price of pens and climate change must be related) but do not make sense in real life, leading to inaccurate data. 4. Launching into the future Before your website can launch, there are a few things you can do to ensure long-term client satisfaction. With conversational commerce on the rise, the use of AI chatbots is definitely a website feature to consider. By utilizing these chatbots to answer online visitors' questions and concerns, you can save clients' time and money. This tool will learn the best ways to interact with people and how certain statements will be received better by some demographics over others. However, chatbots may give your client's site a more impersonal feel and may not be able to help with more complex customer questions. Also, with the rising popularity of AI voice-based searches, websites that implement this software are preferred due to their ease of navigation. When algorithms are programmed to be able to answer common and simple questions, user experience is ultimately optimized, and site owners can give more attention to higher priority issues. There are also tools that help personalize individual visits by learning how specific people use a website. That way, content that is more likely to pique a particular viewer's interest can be recommended. Logical processes are coded in these algorithms so that these programs can still accurately predict what visitors may want to see as time goes on based on previous searches. This algorithm may backfire, though, if multiple people use the same device. What interests a mom of a 12-year-old son will most likely not interest the 12-year-old boy. This won't make much of a personalized experience for either of them. You are not a robot and robots are not web designers. However, the importance of keeping up with the newest web design technology has not diminished one ounce. AI is showing great promise in improving the workflow of web designers everywhere. In no way, shape or form is artificial intelligence perfect. It messes up from time to time just like humans do. There is still much to be gained, though, in the partnership of web designers and AI. Instead of fearing it, designers should embrace it and take advantage of its many functions to enable a quicker, more creative workflow. Although AI still has a long ways to go, web designers can use what is offered today to prepare for what is to come tomorrow. CONCLUSION There are no right or wrong ways of learning AI and ML technologies - the more, the better! These valuable resources can be the starting point for your journey on how to learn Artificial Intelligence and Machine Learning. Do pursuing AI and ML interest you? If you want to step into the world of emerging tech, you can accelerate your career with this Machine Learning And AI Courses by Jigsaw Academy. ALSO READ LightGBM: A Detailed Overview In 4 Points

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5 Industries Which Rely Heavily on Artificial Intelligence and Machine Learning | Jigsaw Academy

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Artificial Intelligence (AI) and Machine Learning (ML) were buzzwords a few years back until tech market players started levering the full potential of these two technologies. Be it Google, IBM, Facebook or any other tech mammoth you can think of, they are putting artificial intelligence and machine learning in front of everything.

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Some examples of industries that AI would or have helped are transport (e.g. metro trains, tesla cars), healthcare (technology that help with diagnosis of diseases, illness, and other sickness), manufacturing and production (helping with producing and making products in factories), and advertising (different types of technologies like big screens, and TVs around the world to help promote businesses).

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Over this period, China’s output of AI-related research increased by just over 120%, whereas output in the US increased by almost 70%.

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In China, the ability offered by AI systems to monitor public spaces and scan Internet traffic in an effort to glean user intentions may provide the state with improved tools for social control, enhancing its capability for monitoring the population or censoring information.

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AI can help us with more accurate measurements and productivity in factories, more accurate and consistent timing for transport, and more efficient advertising, broadcasting, and education.

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Europe, on the other hand, has stringent data laws, which protect people’s privacy, but limit its resources for training AI algorithms.

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Probably many people use AI in the future to the most of it. People would probably even become dependant on AI as it can help out with people's daily lives and work.

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US government expected to spend almost $5 billion on unclassified AI research in 2020 for Defense Advanced Research

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- Reduction of human errors
- Takes risks instead of human
- More accurate procedures and productivity
- Available 24/7
- Do not need as much resources and time like humans
- Digital Assistance
- Faster responses and decisions

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with roughly half as many AI-related papers as Germany in 2015, China crept up over the next three years, then leapt to second place in 2019, showing an increase of 340%.

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- High costs for creation of AI
- Makes human lazy, less productive, and more dependant on technology
- Unemployment

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For someone who would wake up in 2070, they would probably be very dependant on AI. It would be programmed to help people do their chores/things in the morning. For example, make food, pack bags, iron clothes. Humans would probably won't be able to think for themselves anymore and would be relying incredibly on AI. But at the same time, if will help people with their daily lives more faster and efficiently.