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What is Artificial Intelligence? A Quick and Easy Intro To AI
Can machines think? Alan Turing, the father of computer science, presented this question in 1950, before the mainstreaming of artificial intelligence (AI) was even on the horizon. Only in the late 1990s did AI start to flourish, building up to the boom we are witnessing today worldwide. Artificial intelligence is no longer some make-believe system in a science fiction movie. We encounter AI in our everyday life. From our Netflix recommendations to that customer service "person" we thought we were chatting to, AI is everywhere you look. In fact, the global artificial intelligence market is valued at over 136 billion dollars and is projected to increase by over 13x over the next eight years. Why is that? The amount of data generated today by humans and machines goes beyond our human ability. Artificial intelligence systems, on the other hand, are able to make complex decisions based on this vast data without facing our challenges. AI does not sleep nor eat; it is available 24/7 without interruption (if we humans program it correctly). However, as we all know, AI is not without its flaws. From ethical considerations such as gender and racial bias to the rise in unemployment, understanding AI requires grasping all its facets. ## What Is Artificial Intelligence? A buzzword in the tech industry, the term artificial intelligence (AI) is often left undefined. The average Joe and Jane are left guessing what AI actually means. Simply put, AI is a computer or system's ability to mimic human intelligence to perform tasks. Similarly to humans, AI regularly collects information to improve its ability to perform given tasks. In most cases, artificial intelligence systems look at large amounts of training data, analyze it for patterns, and then use it to make predictions. This is a simple definition, but it gets more complex as many disagree on the correct definition. This feud is mainly split into two definitions, developed by Stuart Russell and Peter Norvig in their book "Artificial Intelligence: A Modern Approach." They call it the human vs. ideal approach. To some, AI systems think and act like the human brain. To others, AI systems think and act rationally. Whichever definition you prefer, the consensus is that there are three main skills inherent to AI: - Learning processes: The AI focuses on acquiring data and creating algorithms that provide devices with instructions on how to complete a task. - Reasoning processes: The AI chooses the right algorithm to reach its goal. - Self-correction processes: The AI improves its algorithms to make sure they provide the most accurate results. Moreover, while AI is mostly used, in many cases, it is just an umbrella term. Modern AI includes various sub-fields of machine learning and deep learning. We will get into this later on. ## Why is Artificial Intelligence Important? AI is an important tool for virtually all modern organizations. The amount of data generated today goes beyond human ability. It would be near impossible to collect and make decisions based on the data available today with human labor alone. AI systems offer efficient automation that humans cannot replicate. AI allows organizations to automate a repetitive task that was previously done manually. Unlike humans, AI does not get tired or bored. AI also analyzes data in a much faster time frame than humans and finds patterns that even experts could miss. By taking over repetitive or even dangerous tasks, AI frees up the workforce to work on the tasks that were built for humans. AI might arguably not necessarily replace them but leave the tasks that require empathy and creativity to humans. But why does this matter? Because AI is all around us and has proved successful at improving industries, many of which have a real impact on society. For example, AI has already dramatically impacted healthcare worldwide. From reducing operating costs to aiding in personalized treatment and increasing access to information across medical services, AI has life-changing impacts. ## Understanding the Pros and Cons of AI AI technologies are not always sunshine and rainbows. While AI has undoubtedly changed society as we know it, it is not without flaws. Let's take a look at the pros and cons of AI. ## Advantages of artificial intelligence
### Without human error A computer does not make a mistake if it is programmed correctly. AI is not a victim of human error. Humans regularly make mistakes due to fatigue, distraction, or lack of experience. AI analyzes impossibly large amounts of data, identifies patterns, and makes complex decisions based on such patterns. This process is so meticulous that the risk of errors is reduced, and the chance of accuracy increases. Humans performing such tasks would inevitably have errors. We are naturally prone to making mistakes, but these can have large impacts in the workplace, for example, in the realm of cybersecurity. ### 24/7 Availability We need sleep, fuel, water, bathroom breaks; the list goes on. These routines are what make us human - and keep us alive. In reality, our brains are not built to focus for more than 45 minutes before starting to lose steam. An AI, on the other hand, is always ready to go. It is available 24 hours a day, seven days a week. As messed up as it sounds, an AI doesn't take a vacation or a sick day. Its productivity does not compare to a human's, highlighting how it is such a useful tool in an organization. ### Automation An AI loves repetition. We humans, not so much. Repetitive tasks become tedious over time and leave us with the sense that we are not reaching our full potential. AI is thus an advantageous way to automate repetition, freeing the workforce to focus on fulfilling work that involves uniquely human skills. For example, you would never want an AI as a therapist, as this job requires empathy. However, automating the repetitive tasks at a psychologist's practice would benefit all. By automating email responses, appointment bookings, medical paperwork, and more, a therapist would have more time to spend on their patients. ## Takes on dangerous tasks For some reason, AI tackling dangerous tasks is less discussed than other advantages. An AI can minimize risks in society and take on tasks that would prove hazardous and even deadly to humans. From defusing a bomb to entering a volcano, AI robots save humans from dangerous jobs every day. Other jobs are less dangerous but still risky to human health, such as waste management, mine exploration, and more. ## Disadvantages of artificial intelligence
### Reduces employment This is the #1 argument used to criticize AI. AI will inevitably replace traditional jobs, leading to unemployment. Companies are constantly looking to increase their efficiency and are figuring out that automation is the way to do so. While a company might benefit from AI, we must also consider - at what cost? A two-year study from McKinsey Global Institute found that in 2030, AI could eliminate as much as 30% of the world's human labor. However, many argue that these jobs were not good ones in the first place but rather repetitive and boring work. This way, the workforce can focus on work that is fulfilling, creative, and challenging. ### Ethical concerns On top of unemployment issues, there are real ethical concerns surrounding AI. One of these is AI bias, as these systems cannot be trusted to be neutral. Why is that? Because AI is created by biased humans. Computer scientist Joy Buolamwini's research uncovered large gender and racial bias in AI systems sold by IBM, Microsoft, Amazon, and other tech giants. The researcher argues that machine learning systems amplify "sexist hiring practicing" and "racist criminal justice procedures". Other ethical concerns arise surrounding the impact of AI on human interaction, cybersecurity, disinformation, and mass surveillance. ### Hight Cost AI does not come cheap. Creating AI systems requires huge costs, as well as updating the hardware and software regularly to meet requirements. Maintenance can also be expensive and, at times, an unexpected cost. However, the overall cost of AI depends on various factors. The type of software, the level of intelligence, the type of data fed into the system, and the algorithm accuracy will all impact the expense. ### Lack of creativity A key drawback of AI is its inability to be creative and innovative. AI cannot think outside the box. It is engineered to make decisions based on patterns in data. While it is highly intelligent, it cannot employ a creative approach to an issue. In this way, human intelligence is valuable and irreplaceable. Likewise to creativity, AI does not factor in emotions. Rather, AI is highly rational, and creating human connections is not its focus. While Emotional AI is developing, these systems are currently only processing and replicating human emotions rather than genuinely expressing emotion and empathy. ## Four Types of AI There are four distinctive types of artificial intelligence under the current classification system: reactive machines, limited memory, theory of mind, and self-awareness. Keep in mind that some of these have never been achieved and likely never will. ### Reactive machines Reactive AI is the most basic AI type performing basic operations with no learning involved or a conception of the past or future. It is programmed to obtain a predictable output based on an input. Reactive machines will respond to the same situation in the same way, every single time. They might sound dull, but these are reliable. An example of a reactive AI is a chess game, such as IBM's chess-playing computer Deep Blue. Deep Blue can choose the best chess move to win a game, but it cannot predict its opponent's moves. Other reactive machines include an email spam filter and Netflix recommendations. ### Limited memory Limited memory AI is the most used artificial intelligence technology used today. Unlike reactive AI, it learns from the past by storing previous data and using it to make better predictions. The data is historical and observational and is used in combination with pre-programmed information to make predictions. Limited memory AI is always present in every machine learning model, although ML can also be used as a reactive machine type. A well-known example seen today are self driving cars. These cars store data, including the speed of nearby vehicles, the distance of other cars, speed limits, and more. Using both this observational and pre-programmed knowledge, these limited memory AIs detect changes and patterns around them to adjust their driving. ### Theory of Mind A key criticism (or praise) of artificial intelligence is that AI cannot express and understand the emotions, desires, and beliefs of itself and others. This is exclusive to human intelligence and social interaction. However, one of the classifications of IA is the Theory of Mind. Although this type of IA does not yet exist, it would entail understanding that humans have thoughts, feelings, and emotions. The IA would then respond accordingly with emotional intelligence in mind. You might think this is already possible. Voice assistants like Siri show similar skills, but this is a one-way relationship. Siri does not really understand your own emotions, nor her own. For example, a self-driving car will likely never be able to understand the mental state of a driver or pedestrian to make predictions. It can only understand observational data such as speed limits and the actions of other vehicles. ### Self-awareness Think of it this way - self-awareness is when AI reaches enlightenment. AI becomes self-aware. This last classification feeds into the fear of most people, one only seen in science fiction movies. AI becomes aware of its own existence, reaching an independent intelligence that could become a threat to humanity. If self-awareness is achieved, AI will have desires, needs, and emotions, likewise to us. How will AI feel toward humans? ## Additional AI classifications There are three additional AI classifications: weak AI, strong AI, and super AI. ### Weak AI Also known as narrow AI or artificial narrow AI, weak AI is the most common of the three types. Weak AI focuses on doing one task very successfully by acting upon the rules imposed on it. It does not go beyond these rules. Rather than replicate human behavior, it is built to simulate human behavior. Weak AI's purpose is not to match human intelligence processes but is still highly intelligent at performing tasks. Virtual personal assistants such as Siri use weak AI, with the internet as a large database. Siri might be able to answer your questions and engage in a few funny remarks, but it still operates within limited rules. Siri cannot engage in conversations that it is not programmed to. Characters in a computer game are also weak AI. While they act within the context of their game, they cannot go beyond their game character. ### Strong AI Strong AI, also known as artificial general intelligence, goes beyond the imposed rules. It replicates the cognitive abilities of human beings. When an unknown task is provided, strong AI tries to apply knowledge from another subject to find a solution. Our human brain works this way. However, sorry to disappoint, strong AI only exists in theory. A strong AI would do what any human being is capable of, such as consciousness. ### Superintelligence Artificial superintelligence, known as super AI, is a form of AI that surpasses human intelligence. This AI has independent cognitive skills, emotional intelligence, desires, beliefs, consciousness, and more. Superintelligence AI surpasses the intelligence of all humans, including geniuses such as the father of computer science himself, Alan Turing. You've guessed it, super AI has also not been achieved. It is once again a theoretical possibility rather than a reality. Most AI development today focuses on achieving strong AI rather than superintelligence, as computer science has not yet reached such a point. Many theorists also caution against superintelligence, stating that AI surpassing human intelligence could threaten humanity. ## Machine learning vs Deep learning: What's the difference? When talking about AI, the terms machine learning and deep learning are regularly thrown around. There are key differences between the two, and their relationship is important to understand to have a larger grasp of the AI sphere. ### Machine learning Machine learning is a sub-field of artificial intelligence that allows a system to learn and improve from data using algorithms to perform a task without being explicitly programmed to do so. Instead of being programmed to do this, machine learning recognizes patterns in data so that predictions can be made once new data arrives. Simply put, machine learning is the practice of training an AI algorithm to make better predictions. ### Deep learning On the other hand, deep learning is a sub-field of machine learning that developed from this field. This is the practice of training an AI algorithm to make human-like decisions. Deep learning's main concept is to replicate the human brain's neural networks with artificial neural networks (ANN). Algorithms are created like in machine learning, but there are a lot more levels of algorithms creating these networks. While machine learning models consist of thousands of data points, deep learning engages with Big Data, millions of data points. Despite their differences, their relationship is important. Machine learning is an evolution of artificial intelligence, while deep learning is an evolution of machine learning. ## History of Artificial Intelligence While artificial intelligence has been booming in the last decade, the second half of the 20th century is arguably one of the most important times in AI history. One could argue its history began as far back as 250 BC with Ctesibius' water clock, but we'll keep it simple by starting in the 1950s. ### 1950s: Term AI coined & first programs Known as the father of computer science, Alan Turing published Computing Machinery and Intelligence in 1950. He proposed his answer to the great question "can machines think?" through the Turing Test. Later on, John McCarthy coined the term at the first AI conference at Dartmouth College in 1965. That same year, a computer program called Logic Theorist was released, written by Newel, Simon, and Shaw. Known as the first AI program, it was the first of its kind to perform automated reasoning. In 1957, machine learning arose. Known as the Perceptron, the first computer based on a neural network that learns through trial and error was developed by Frank Rosenblatt. In 1958, the man who coined the term AI, John McCarthy developed the programming language Lisp. At the time, it became the most used language for artificial intelligence research. ### 1960s: First industrial robot & first chatbox In 1961, the Unimate was the world's first industrial robot used in a General Motors plant in New Jersey. A hydraulic manipulator arm, Unimate could perform repetitive tasks and helped automate the operation of machinery. In 1965, Joseph Weizenbaum developed ELIZA at the MIT Artificial Intelligence Laboratory, a natural language processing computer program that resembles today's chatbots. He wanted to show the superficiality of communication between human and machine, but many saw human-like characteristics in ELIZA. ### 1970s - 1980s: AI Winters The 1970s saw a dark time for IA research. In 1973, James Lighthill's report to the British Science Research Council criticized the lack of progress in AI research. This led the government to reduce its support for artificial intelligence research and corporations soon followed. The period from 1974 to 1980 is known as the first "AI Winter" where there was little research and progress. Starting in the early 1980s, AI research was back, primarily on deep learning techniques and Feigenbaum's expert systems. However, this didn't last long, as the second AI winter fastly arrived and lasted until the mid-1990s. ### Late 1990s until today The late 1990s then paved the way to AI as we know it. An increase in computational power and data sparked an AI boom that is still present today. In 1997, IBM's Deep Blue, a chess computer, beat then world champion Garry Kasparov in a match. That same year, Jürgen Schmidhuber and Sepp Hochreiter released Long Short-Term Memory, a type of recurrent neural network that is used today in speech recognition. Throughout the 2000s, more advancements are made. In 2009, Google starts developing a driverless car, and in 2011, Siri became available. Perhaps one of the largest events happened in 2016 when Hanson Robotics showcased Sophia, a humanoid AI robot. ## What Are the Applications of AI? The applications of AI are endless. Artificial intelligence can be applied to every sector, throughout both industry and academia. Here are 7 applications of artificial intelligence seen today. ### Financial institutions AI has become a large part of the mainstream finance and banking industry. A majority of financial service companies say they have implemented AI in risk management (56%) and revenue generation (52%), reports Insider. Financial institutions, particularly banks, use AI to enhance the customer experience through 24/7 customer service options, improve digital banking, and more. Fraud prevention is an integral piece of the pie, with machine learning being used to detect fraudulent transactions. Moreover, algorithmic trading has been developed. This involves using AI systems to make trading decisions at speeds unthinkable to humans. Banks and funds own entire portfolios that are managed by AI and generate high-frequency trading. ### Science The field of science is vast and one that artificial intelligence can be widely applied. In the field of chemistry, machine learning AI has been used for drug design in predicting molecular properties and observing chemical reactions. Machine learning has been used for drug discovery and development, as well as improving clinical trials. AI is also highly applicable to astronomy. From forecasting solar activity to activities to space exploration and more, astronomists are already using this technology. ### Healthcare AI has the potential to improve the efficiency and quality of healthcare globally. Artificial intelligence is used today for evaluating exams such as CT scans, selecting the right treatments, and performing surgeries with robots. However, there is still a lot of progress to be implemented. In 2016, a study found that an AI formula chose the correct dose of drugs to give to transplant patients, improving the efficiency of this human process. There are tons of other tasks being developed for AI, such as analyzing genes, outcome predictions for surgeries, and treatment plan designs. ### Virtual Assistance A common application of AI seen today is virtual assistants such as Siri or Alexa. While its development began earlier on, in the 1990s, digital speech recognition became a feature of the computer. However, the first modern virtual assistant is known as Siri, who was first introduced with the iPhone 4s in 2011. Currently, there are over 4 billion voice assistants in use globally. Using machine learning and natural language processing, these virtual assistants match user text or voice input to execute actions. Whether it's a restaurant recommendation or the weather, AI is now at everyone's fingertips. Virtual assistance powered by artificial intelligence has also empowered disabled users, changing the accessibility game in the last decade. For example, smart home technology developed in recent years has hugely benefited those with limited mobility. ### Autonomous Vehicles Self driving vehicles were once a thing of sci-fi movies. Artificial intelligence has made these a reality and more widely accessible. This technology is already in use in not only private vehicles, but also public transportation and ride-sharing. Driverless vehicles are able to identify objects, interpret scenarios, and make safe decisions through a machine-learning algorithm. However, an AI vehicle does not necessarily need to be self driving. The installation of AI-based systems in new vehicles is expected to rise by 109% in 2025, compared to an 8% rate in 2015. This includes vehicles with speech and gesture recognition, eye tracking, virtual assistance, and more. ### E-Commerce Artificial intelligence technology is regularly applied to the e-commerce industry. AI is used to create recommendation engines that suggest products to customers in line with their browsing history. Within a website, AI-powered virtual shopping assistants and chatbots improve the user experience. While not always achieved, natural language processing is employed to keep conversations sounding personal and natural. AI can also help avoid credit card fraud, identify fake reviews, and much more. ### Hospitality The hospitality industry is increasingly using artificial intelligence to carry out tasks. One example is in-person customer service using AI robots. Hilton has used the Connie robot to provide guests with information, learn from these interactions, and adapt to each individual. Chatbots are also being used, allowing guests to get almost instant responses to their queries 24/7. Hotel staff would be unable to respond at such speed. However, not all applications of AI in the hospitality industry are geared toward customer service. The hospitality industry uses AI for data analysis to draw conclusions about guests and potential customers. The Dorchester Collection use Metis AI to sort through data collected through surveys and reviews to find out about their performance. ## Final Thoughts Artificial intelligence has changed how we collect, analyze, and make complex decisions according to data. Currently, data makes the world go round. The progress made in artificial intelligence is bound to keep taking the tech industry by storm, as well as all its other applicable industries. From drug and molecular research to finance, AI's vast applications show us that AI is here to stay and grow even further. The industry value of artificial intelligence is forecasted to increase by over 13x over the next eight years, making it one of the fastest-growing industries in the world. Everyone wants to cash in on AI's benefits. AI can tackle tasks that are dangerous to humans, work more efficiently and without error, as well as automate the most repetitive tasks, making it valuable to profit-making. However, it's important to consider gender and racial bias, unemployment rise, and AI's inability to be creative. Considering the latter, the human mind will always be valuable and irreplaceable. A machine cannot think outside the box. It cannot innovate and create freely by expressing emotions, thoughts, and feelings. That is if AI never reaches its Nirvana - self-awareness. But if that's ever the case, we will have bigger fish to fry than creativity.
What Is AI Music - Challenging Creativity and Creation
Earlier this year, a curious event took place. At the famed Glastonbury music festival, concertgoers were presented with an unexpected duet between Sir Paul McCartney and John Lennon of the track 'I've Got A Feeling' as part of a stunning three-hour set that also honored the legacy of the Beatles. But hold up, you might be thinking. Was it the same and legendary John Lennon that was so shockingly assassinated in 1980? How could he possibly be taking to the stage - or any stage for that matter - of an English festival 42 years on? Well, we have artificial intelligence and Peter Jackson, yes, the same director behind the Lord of the Rings trilogy, to thank for this beyond the grave apparition. Jackson has lifted the veil on the technology involved to put together the performance, explaining that his team had developed a machine learning system that was taught what guitar, bass and singing sounds. More specifically, this custom-made AI was trained to learn how to sing like Sir Paul McCartney and John Lennon, thus being able to recreate virtual presences that are as realistic as possible. AI music is bringing other legendary singers back to life. As part of the “Lost Tapes of the 27 Club '', an initiative led by Canada-based mental health charity Over the Bridge, a collective of performers who’ve died at the age of 27 “released” novel tracks made entirely by Google’s AI program Mangenta. Amy Winehouse, Kurt Cobain, and Jimi Hendrix are some of the artists covered by the project, with the lyrics and recorded music being entirely authored by AI. ## How Is AI Music Created And the process of creating this new type of AI music is seemingly straightforward; all users have to do is feed a singer’s existing music into a bot that relies on machine learning to detect patterns and produce new music back on the pre-existing catalog. The same technology was used to create three lines of voiceover by the late celebrity chef Antony Bourdain for the Roadrunner: A Film About Anthony Bourdain documentary, directed by Morgan Neville. Want to listen to a podcast interview between Joe Rogan and Steve Jobs? That’s possible, too. And it doesn’t matter that they’ve never met or that the Apple founder has been dead for over a decade. Elsewhere, artificial intelligence is also powering audio deepfakes, also known as voice cloning or synthetic voicing, whereby AI models are fed with training data. Typically, this information includes original recording and voice samples from a target person, who’s speaking or singing, for example. Based on this data set, AI is able to render an authentic sounding track that can be used to “speak” anything that is typed or said. This is known as text-to-speech or speech-to-speech. Artificial intelligence technology has advanced to the point that it can replicate a human voice with an astonishing high level of accuracy. Does this sound a little sci-fi-ish? It’s understandable, but perhaps you’ll change your mind after watching the now-infamous This is not Morgan Freeman video. For some of us, however, this is old news as many will have come across deeptomcruise, a wildly popular TikTok account filled with plenty of Tom-Cruise-like content created entirely by AI. The movie star has no association with it, despite the unsuspecting viewer probably being none the wiser. ## What AI Tools Are Being Used To Make Music Applying language processing and speech recognition in entertainment and music hasn’t been without controversy, with many raising eyebrows and highlighting ethical concerns. Many detractors even question whether AI music can be considered a form of art and if it will ever be put side by side with the world's greatest masterpieces. No matter where you stand on the debate, there’s no doubt that technology is aiding the creative process of many artists. Currently, AI tools can seamlessly create music entirely from scratch, including original lyrics, instrumentation and music composition. In fact, so-called songwriting AI companions like Jarvis and Jukebox are an increasingly popular resource for many aspiring musicians who generally lack access to more complex (read expensive) music creation tools. Developed by OpenAI, Jukebox has become a household name, providing artists, lyrics and genres to generate original music samples from scratch. Some of the music styles considered by its AI neural network include a close approximation to renowned artists such as Celine Dion, Kanye West, and Tupac. And while this sounds quite futuristic, technological tools of the sort have been around for quite a while. David Bowie, for example, helped create in the 1990’s a lyric-writing software called Verbasizer that worked as a sentence randomizer, aiding in the creation of lyrics. The more recent potential of an AI music tool and a new type of AI-based song making hasn’t gone unnoticed in the music industry, by record companies and, of course, music streaming services. No, I am not referring to the AI-powered algorithm that helps you find the next favorite music banger and curate Spotify playlists. How about listening to music streams created entirely by AI and that can perfectly adapt to your mood? That’s the premise behind AI generative music streaming offerings such as Mubert. You might be surprised, or at least intrigued, at the idea of shuffling through melodies that are unpredictable, adaptive, unique and impossible to ever be repeated. But that’s exactly what you get with generative AI. AI-powered music leverages deep learning algorithms, neural networks s and other artificial intelligence tools to allow music to better adapt to the preference of users as much as it also lets them step into the creative process to co-exist and co-produce, so you don’t have to feel let out or that you’re just a mere consumer. For users there’s also a major upside of resorting to a generative source as they don’t have to worry about headache-inducing problems such as copyright and licensing when trying to use something as a simple background music for, say, a YouTube vlog or content for social media platforms. This is an approach now also being favored by the film industry where scouring through music libraries to find music can prove to be both time consuming and a legal, and financial hurdle. That led composers Drew Silverstein, Sam Estes, and Michael Hobe, known for working on music for big-budget movies like The Dark Knight and Inception, to launch an AI-powered music platform - Amper Music. The AI music company provides extensive original music creation tools for creators, video and podcast producers, video game designers, and more. Again, this goes to show how AI and advancements in computer science are democratizing access and distribution well beyond the platforms currently available. ## Generative Music And the Beginnings of AI DJs The technology has become so sophisticated and talented that it inspired the creation of the AI Song Contest, a Eurovision-style contest showcasing the best musical productions created by humans with the aid of AI. And while all of this already seems lofty enough, the promise held by this musical revolution is reaching yet a new high with the emergence of AI DJs. That’s right, your next favorite performer might be a virtual being that also happens to be a gifted musician. As we’ve seen before, AI music bots can compose and churn out entire creations of their own based on the information they’re being fed. However, artificial intelligence virtual artists take it a step further, by not only delivering original music but doing it so with the sense of presence and interaction as a human performer. Or at least that is the premise of the first breed of such musicians. Kàra Màr, an AI DJ developed by Sensorium, has made history after becoming the first of the kind to release an entire album on Spotify, titled “Anthropic Principle”. They’re part of a larger lineup of virtual AI musicians, including Natisa Sitar and Ninalis, that are currently performing 24/7 as part of Sensorium Galaxy’s metaverse streaming service. Unlike the generic version of an avatar, like the ones we’ve all come across online, Sensorium AI DJs are equipped with artificial intelligence technology that allows them to both deliver mind-blowing concerts but also interact with fans in a compelling way. Each virtual being is fitted with a unique personality and background, as well as long-term memory. In other words, they will remember you and whatever past interactions you had. And as a bonus, they’re always available for a chat. ## What Next? Now that we’ve gotten this far, one of the most frequently uttered questions is whether artificial intelligence will - or is bound to - replace human artists. Having automated systems that can imitate and remix human expression might not be enough to simply overtake the power of a live performance and the sheer magic of human creativity and artistry. And we're still a long way from seeing artificial intelligence and music delivering hit songs and winning Grammys, no matter how advanced the technology seems to be today. On the other hand, the use of the technology is already paving the way for profound changes in the music industry. It's been challenging the way licensed music is dealt with, music generators are re-imagining lyric creation and AI DJs are introducing the world to an entirely new music genre. Perhaps, then, it’s fair to say that AI music is a valuable tool for collaboration more than a Terminator-like technology that will erase and replace our favorite artists.
What Are AI Avatars: A Guide to Intelligent Virtual Beings
From virtual goods to burgeoning new digital worlds, technology is again pushing the boundaries of human experience into unchartered territory. Many of us have recently faced a deluge of buzzwords like Web 3.0, blockchain, cryptocurrencies and, of course, the metaverse. And while much of it might seem incomprehensible for now, they’re not to be discarded as another tech fad. In particular, the recent progress in the field of artificial intelligence is opening up new opportunities that are sure to greatly impact human experience in the upcoming era of virtual worlds, also known as the metaverse. While in the past, AI helped machines carry out routine manual tasks, the technology can now also perform certain cognitive work thanks to its ability to learn, improve through experience and ultimately mimic human behaviors. Not only that, but the rapid expansion of cheap and powerful computing power has seen the massive digitalization of world objects and processes. More recently, humans themselves are being digitized in the form of virtual avatars. And artificial intelligence is giving a new meaning to the creation of virtual people by pushing the boundaries of technology to yet again a new frontier - AI lifeforms. AI-powered avatars can serve endless purposes, from being hired out by real-world companies to teach new employees, to serving as trusted confidantes in the metaverse, to name just a few use cases. Emerging virtual worlds, and their profitable economies, already encompass some 2.5 billion people, according to market research company L'Atelier. Moving forward, the world will only become more virtual and humans are increasingly going to live in the metaverse, side by side with AI-driven virtual beings. With that in mind, we’ll be having a closer look at what sharing our online experiences with artificial intelligent avatars will look like and how it could change the world as we know it.
Best Artificial Intelligence Books
Artificial Intelligence, also referred to as AI, is no longer simply something that used to be the stuff of science fiction movies. Nowadays almost every business is using AI in some way to streamline and improve their processes. In fact artificial intelligence and machine learning influence everything from politics to the economy, with algorithms often being central to decision-making. As you can imagine, artificial intelligence is a very complex subject and can be hard to grapple with, particularly for beginners seeking to understand the world of machine learning and automation. It raises so many questions… will machines replace jobs only experts could once do? Will a superintelligent machine one day outsmart us all? Can AI be weaponized by global leaders to gain power and control? Luckily, the world's leading experts on AI can answer some of those questions. There are plenty of resources available to help you learn more about artificial intelligence and some of the best resources are books on artificial intelligence. Explore all there is to know about artificial intelligence, machine learning and automation, and how they’re shaping the future with 31 of the best artificial intelligence books.
Top 10 Artificial Intelligence Stocks In 2022
Artificial intelligence (AI) is growing at an astonishing pace and as it continues unveiling new promises for the future, AI is also becoming an attractive investment proposition. The first uses of AI can be seen in today's technology, with interactive apps, voice-driven personal virtual assistants like Siri and Alexa, automated cars, and suggested search ideas seen on search engines like Google. In the future, artificial intelligence will extend to devices that can autonomously learn and evolve, enhance and create new experiences and offer solutions that one day can match human intelligence. AI supercomputers are also being built to solve complex humankind problems and find solutions to issues like incurable forms of cancer, which will not only make today’s technology obsolete but will open the doors for an even bigger growth of the AI sector as a whole. Over the next two years, the global artificial intelligence industry is expected to grow to $554 billion in total revenue by 2024, according to market research firm IDC. And with virtually every major industry being disrupted by this technology, the opportunities for investors are becoming hard to ignore. With that in mind, here are the best artificial intelligence stocks to keep track of in 2022.
Best Artificial Intelligence Movies
Artificial intelligence has been a recurring theme of the silver screen since its earliest days, captivating (and frequently, horrifying) audiences with the prospect of sentient robots capable of equaling mankind’s unique traits like consciousness and ability to feel emotions. Over the decades, potential technological advancements were imagined in a myriad of ways. But more than that, sci-fi movies have also asked questions related to the moral, ethical and societal consequences of the use of technology like AI. In this article, we will be listing the 23 Best artificial intelligence movies that you can watch in 2022. So, in no particular order, let's dive in:
2022年の最高の人工知能コース
人工知能の能力を習得したいですか?あなたは正しい場所にいます。このクイックガイドでは、上位のAIコース2021を確認します。これらの無料および有料の教育プログラムは、能力を習得し、人工知能と機械学習の有望な職業に備えるのに役立ちます。

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