Artificial Brain

artificialbrain

Honorio Apaza, a 29-year-old from Puno, Peru, battled poverty from a young age and emerged as a successful systems engineer. He’s now known for creating an (AI)-based translation model for translating Aymara, a language indigenous to the Andes, into Spanish and English. artificial intelligence Aymara, Apaza’s native language, is spoken by around 2 million people across Argentina, Bolivia, Chile, and Peru. “There’s no longer a communication barrier between an Aymara speaker and an American. Technology enables that communication,” the Peruvian AI expert told . Peru Reports Hailing from the rural community of Tarapoto, Apaza developed an AI model called “AI-MARA” that amazed scholars worldwide. He was recruited by Venga Global, a company working on Google’s Aymara-to-English translation. (Google Translate has already added Aymara and Quechua, another Indigenous language in Peru, to its list of languages). “I knew what Google did with its AI model and decided to create a new system from data. AI learns like teaching a child to speak. I gave translation examples, taught terms in parallel between two languages for training,” he explained. Apaza developed his AI model in about nine months. “I created a kind of artificial brain that learns to write. It doesn’t memorize, it learns to put words together to make sense and coherence.” “The most updated version of this model has a database of over 18,000 sentences, and we’ll soon publish this new research in a specialized journal,” he stated. His interest and expertise in could be seen as a surprise, given his family’s rural, impoverished background. Apaza comes from a village of about 70 people, with cattle farming being the main economic driver. technology During his teens, he went to live with extended family in Ilo, a small port city in Southern Peru, where he began studying systems engineering at the National University of Moquegua, named after the Moquegua Region where Ilo is located. To support his studies, he sold ice creams and other products in the street. “I applied and got in after a lot of effort,” he said. “Since I lacked resources for university expenses, for several months, I sold the ice cream my aunt made on the street. I worked as a bakery assistant, bus conductor, waiter…” Apaza soon earned a scholarship, covering his university expenses, and he went on to earn a degree. He became enamored with the possibilities of AI and went to at a technical institute, before leaving to study artificial intelligence at the Universidad Católica San Pablo in Arequipa, Peru. work as a teacher This led him to, eventually, immerse himself in AI-powered Aymara to Spanish and English translations. “My work, beyond validating Aymara-to-English translations, aims for a future where documents can be written in native languages like Aymara or Quechua using AI. The AI could generate documents, records, reports, just like ChatGPT,” he said. According to Apaza, his work requires financial support from Peru’s government, as it involves working with many linguists, and translators, as well as visits to far flung Indigenous communities to understand words only elderly inhabitants use. “The idea is to generate a much richer and train AI with it. The AI model would be much more efficient,” he explained. dataset Apaza also told that he’s developing an app, similar to Duolingo, for teaching Aymara “but if we don’t have well-structured data, unfortunately, it won’t be possible. I’m thinking of approaching the Ministry of Culture,” he said. Peru Reports Peru has made some progress in AI development and use cases, mainly in private sectors like banking, telecommunications, retail, manufacturing, and mining, as well as small progress in university research. According to the “National Artificial Intelligence Strategy (ENIA)” report by the Peruvian government in 2021, AI is in an intermediate stage of development in Peru, and its full potential hasn’t been explored across all industries. published Both the industrial sector and academia, along with the government, recognize deficiencies in factors driving technology, like infrastructure, connectivity, data-based culture, and access to high-quality open data, as stated in this document. The young systems engineer is enthusiastic about contributing to AI research and application in Peru and encouraging institutions to use and develop AI for benefits in various fields like economy, medicine, and construction. “AI has a vast field ahead. In Peru, it’s time to start creating technology, not just using it. Many of the current problems in Peru could be solved more efficiently with AI,” Apaza said. This article was originally published by on Diego López Marina The Sociable.

Peruvian Engineer's Inspiring Journey: Translating Aymara to English with Google AI

I admit that the title may seem strange at first. Why are Brain-Computer Interfaces among humanity's most important innovations ever made? Just consider fusion energy, which could provide us with infinite energy. Wouldn't an infinite amount of energy be more critical? But do we need an infinite amount of energy? Strictly speaking, an individual’s minimal resource requirement is not high. On average, one person needs around 2 x 1 x 1 meter of space and enough energy to keep it warm. With proper insulation, this would be minimal. If we stay still instead of moving, our nutrient intake would also drop to a minimum. Not needing disposables would reduce our ecological footprint to zero and almost eliminate the growing pile of waste surrounding us. For the nature around us, having us out of the equation would be the ‘ideal state’. But who would want to live their whole life in a coffin?! Though this might sound daunting at first, let’s look closer. What is missing? Freedom of movement? Not necessarily. What is actually lacking is the input and interaction with each other and the world around us. In essence, our brains - which are us - remain stationary in our heads for the entirety of our lives. It transmits impulses down our limbs and receives input from our senses. If a computer could process and create all of these impulses in the same quality that we experience in reality, our brain would feel just the same as it does now. If a large portion of our consumption was virtual, we could immediately get rid of many associated problems. Take the meat industry for example. The meat industry contributes significantly to CO2 emissions and requires considerable areas taken from nature. A large part of the animals living on Earth are now kept for profit, and the large majority of wild animals are threatened with extinction, as their habitat is shrinking. However, meat is not essential. Many vegetarians on Earth never eat meat, yet they are fine. What we really need is not meat, but the feeling of eating meat. What if we could separate the act of eating from the intake of food? The necessary nutrients would be produced locally, thus no need for packing, shipping, etc. The food production process would be completely sustainable. And, if someone was craving a good burger, they would be able to eat it in the virtual space. They would experience the flavor of the burger, the way the juicy patty melts in their mouth, so they would feel the same experience as if they were eating a real burger. Just imagine how many problems this single change could solve. As almost every problem of mankind stems from overconsumption, if we could shift a significant part of our consumption into the virtual space, we could solve almost every problem of mankind. Is this too sci-fi? For now, yes. But the research in is a very active area of study, so it may become possible within a few decades what I've written about. brain-computer interfaces In the following section, I will discuss where BCI research stands today and what can be expected in the future. Research on BCIs has an important topic of curing blindness. If we were able to project the image of a camera into the brain of a blind person, this would restore their ability to see. Likewise, if we were able to do this for a blind person, we would also be able to do so for a healthy person. There would be no need for VR headsets if we could project the image directly into the brain. And if we were able to project images into the brain, we could also simulate tastes, smells, and other sensations to a complex degree, such as the experience of eating a hamburger. But is this really possible? Everyone has probably heard of Elon Musk’s , where the ultimate goal is to establish a brain-computer connection of this magnitude. But they are not the only ones working on this technology. Similar technologies are being developed by BlackRock Neurotech and Synchron. Neuralink The above three companies are developing invasive technology, hence surgical intervention is required to install the devices, though the aim everywhere is to make this intervention as minimal as possible. However, there are explorations of non-invasive solutions as well, which do not require opening the skull. According to , it is possible to project images into the visual cortex, which is the part of the brain responsible for vision, using focused ultrasound. For this, special viruses must be used to deliver proteins to the neurons that make them sensitive to ultrasound. new research The , supported by DARPA, started with a similar goal, aiming to create a kind of visual telepathy, but using near-infrared light and magnetic fields instead of ultrasound. In this case, viruses would be employed to make neurons sensitive to light and magnetism so they could be stimulated externally. MOANA project Who knows what the future holds?! It is possible that we will be able to access neurons without viruses with the help of nanorobots, or we might use nanoparticles that will be carried by the blood to the brain where an external magnetic field will be used to build wires from them, providing a direct connection to the brain through the back of the neck, somewhat like in the MATRIX. The realization of brain-computer interfaces is a technological problem. We need to connect an organic machine (the brain) to one based on silicon. Although the problem is complex, it appears tractable and the benefits would be fascinating. For many, this may be a frightening prospect, but this technology could actually liberate us. With the help of brain-computer interfaces, overconsumption, energy problems, and climate change could be reversed. We could solve many problems at once with a single technology, so I believe it is worth focusing more of our resources on this area since in a certain sense brain-computer interfaces are the holy grail of science and technology. And finally, a short sci-fi story: By 2030, humanity had been driven to the brink of extinction. The pollution of the environment and the destruction of Earth's resources had reached such an extent that irreversible changes had been made to the billions-of-years-old ecosystem. The leaders of the world had done almost nothing to halt the catastrophe. They had been blinded by money and power, and the people had been deceived by the empty promises of their leaders. Eventually, the smog had completely shrouded the sun, the soil had become infertile, and famine and destitution spread throughout the land. Due to the extreme weather conditions, more and more places had become uninhabitable, wars had broken out, and countries had been brought to collapse. It was the very hell on Earth, but still, many people kept proclaiming: this is not our fault. In their desperation, the world's major tech companies and scientists joined forces in an unprecedented attempt to find a solution. Everyone pinned their hopes on the increasingly advanced artificial intelligence, and eventually, it was the AI who spoke what everyone had been afraid to say: the only way out is to remove the most harmful component, humans! They created a perfect virtual world, the MATRIX, where everyone could live happily ever after. There was no want, no famine, no diseases. Anyone was able to join the network for free. Those who made the decision had nanobots injected into their bodies, connecting their brains and then linking them to the MATRIX through a port in the back of their neck. While people were living in perfect abundance in the virtual world, their bodies were being sustained in a self-sustaining ecosystem. Initially, the MATRIX was the heaven for the people, but they could not make sense of their omnipotence and soon grew tired of the perfection. In the end, they decided to erase their previous memories and start a new life in a '90s-style world where they had to work for everything, but also be happy with what they had achieved. The people were happy again in this simulated world they had created, where they could finally forget the horrors of the outside world. With few exceptions, the entire human race had joined the MATRIX, with only a few smaller colonies like Zion having rejected the technology. They lived underground in cramped bunkers subsisting on a tasteless protein shake. It was a living hell. Refusing to accept the comforts of what they considered an insidious world, they had fabricated some strange children's story about the machines forcibly dragging people into the MATRIX to be used as some kind of energy source. Although it was plainly contradictory - they had, after all, access to sources like nuclear energy - everyone believed it and propagated it. They were sick people, the products of a sick world. For a long time, the two worlds coexisted peacefully until a small group of self-proclaimed heroes set out to 'save' humanity from the MATRIX. This is now their story...

Why Brain-Machine Interfaces Will Be Human Kind’s Greatest Invention

For many healthcare providers, the industry is shaping up to be more of a shifting quandary of regulatory issues, financial turmoil, and unforeseeable eruptions of resentment from practitioners on the edge of revolt. The industry is now taking the opportunity to scale up their big data defenses and develop the technological infrastructure required to meet the imminent challenges. However, the struggle to make sense of the data collected in the process might rage on for years. Since the healthcare system has started adopting cutting-edge technologies, there is a vast amount of data collected in silos. Healthcare organizations want to digitize processes, but not unnecessarily disrupt established clinical workflows. Therefore, we now have as much as 80 percent of data unstructured and of poor quality. This brings us to a pertinent challenge of data extraction and utilization in the healthcare space through various applications of natural language processing in healthcare. This data as it is today, and given the amount of time and effort it would need for humans to read and reformat it, is unusable. Thus, we cannot yet make effective decisions in healthcare through analytics because of the form our data is in. Therefore, there is a higher need to leverage this unstructured data as we shift from fee-for-service healthcare model to value-based care. This is where Natural Language Processing, a subcategory of Artificial Intelligence can come in. already possess the capabilities of well and truly mimicking human behavior and executing a myriad of tasks. When it comes to implementing the same on a much larger use case, like a hospital – it can be used to parse information and extract critical strings of data, thereby offering an opportunity for us to leverage unstructured data. NLP based chatbots This augmentation could save healthcare organizations precious money and time by automating quality reporting and creating patient registries. Let’s explore the various applications of natural language processing in healthcare and its possible benefits to the industry. Applications of NLP in Healthcare Studies show that Natural Language Processing in Healthcare is expected to grow from , at a CAGR of 20.8 percent during the forecast period. USD 1030.2 million in 2016 to USD 2650.2 million in 2021 NLP, a branch of AI, aims at primarily reducing the distance between the capabilities of a human and a machine. As it is beginning to get more and more traction in the healthcare space, providers are focusing on developing solutions that can understand, analyze, and generate languages can humans can understand. There is a further need for voice recognition systems that can automatically respond to queries from patients and healthcare users. There are many more applications of NLP in Healthcare as elucidated below – Handle the Surge in Clinical Data The increased use of patient health record systems and the digital transformation of medicine has led to a spike in the volume of data available with healthcare organizations. The need to make sense out of this data and draw credible insights happens to be a major driver. Support Value-Based Care and Population Health Management The shift in business models and outcome expectations is driving the need for better use of unstructured data. Traditional health information systems have been focusing on deriving value from the 20 percent of healthcare data that comes in structured formats through clinical channels. For advanced patient health record systems, managed care, PHM applications, and analytics and reporting, there is an urgent need to tap into the reservoir of unstructured information that is only getting piled up with healthcare organizations. NLP in Healthcare could solve these challenges through a number of use cases. Let’s explore a couple of them: – Electronic Health Record solutions often have a complex structure, so that documenting data in them is a hassle. With speech-to-text dictation, data can be automatically captured at the point of care, freeing up physicians from the tedious task of documenting care delivery. Improving Clinical Documentation – Computer-assisted coding can be improved in so many ways with NLP. CAC extracts information about procedures to capture codes and maximize claims. This can truly help HCOs make the shift from fee-for-service to a value-based model, thereby improving the patient experience significantly. Making CAC more Efficient Improve Patient-Provider Interactions with EHR Patients in this day and age need undivided attention from their healthcare providers. This leaves doctors feeling overwhelmed and burned out as they have to offer personalized services while also managing burdensome documentation including billing services. Studies have shown how a majority of care professionals experience burnout at their workplaces. Integrating natural language processing with electronic health record systems will help take off workload from doctors and make analysis easier. Already, virtual assistants such as have made it into healthcare organizations, working as administrative aids, helping with customer service tasks and help desk responsibilities. Siri, Cortana, and Alexa Soon, NLP in Healthcare might make virtual assistants cross over to the clinical side of the healthcare industry as ordering assistants or medical scribes. Empower Patients with Health Literacy With , a key use-case and benefit of implementing this technology is the ability to help patients understand their symptoms and gain more knowledge about their conditions. By becoming more aware of their health conditions, patients can make informed decisions, and keep their health on track by interacting with an intelligent chatbot. conversational AI already being a success within the healthcare space , researchers used NLP solutions to match clinical terms from their documents with their layman language counterparts. By doing so, they aimed to improve patient EHR understanding and the patient portal experience. Natural Language Processing in healthcare could boost patients’ understanding of EHR portals, opening up opportunities to make them more aware of their health. In a 2017 study Address the Need for Higher Quality of Healthcare NLP can be the front-runner in assessing and improving the quality of healthcare by measuring physician performance and identifying gaps in care delivery. Research has shown that artificial intelligence in healthcare can ease the process of physician assessment and automate patient diagnosis, reducing the time and human effort needed in carrying out routine tasks such as patient diagnosis. NLP in healthcare can also identify and mitigate potential errors in care delivery. . A study showed that NLP could also be utilized in measuring the quality of healthcare and monitor adherence to clinical guidelines Identify Patients who Need Improved Care Machine Learning and NLP tools have the capabilities needed to detect patients with complex health conditions who have a history of mental health or substance abuse and need improved care. Factors such as food insecurity and housing instability can deter the treatment protocols, thereby compelling these patients to incur more costs in their lifetime. The data of a patient’s social status and demography is often hard to locate than their clinical information since it is usually in an unstructured format. NLP can help solve this problem. NLP can also be used to improve care coordination with patients who have behavioral health conditions. Both, Natural Language Processing & Machine Learning can be utilized to mine patient data and detect those that are at risk of falling through any gaps in the healthcare system. Since the healthcare industry generates both structured and unstructured data, it is crucial for healthcare organizations to refine both before implementing NLP in healthcare. How Would Healthcare Benefit from NLP Integration? Natural Language Processing in the healthcare industry can help enhance the accuracy and completeness of EHRs by transforming the free text into standardized data. This could also make documentation easier by allowing care providers to dictate notes as NLP turns it into documented data. is another excellent benefit of NLP in healthcare. It can be viewed as a silver bullet for the issues of adding significant detail and introducing specificity in clinical documentation. For providers in need of a point-of-care solution for highly complex patient issues, NLP can be used for decision support. An often-quoted example and an epitome of NLP in healthcare is IBM Watson. It has a massive appetite for academic literature and growing expertise in clinical decision support for precision medicine and cancer care. In 2014, IBM Watson was used to investigating how NLP and Machine Learning could be used to flag patients with heart diseases and help clinicians take the first step in care delivery. Computer-aided coding Natural Language Processing algorithms were applied to patient data and several risk factors were automatically detected from the notes in the medical records. Since there is this explosion of data in healthcare which pertains not only to genomes but everything else, the industry needs to find the best way to extract relevant information from it and bring it together to help clinicians base their decisions on facts and insights. What the Future of Natural Language Processing in Healthcare Looks Like NLP in Healthcare is still not up to snuff, but the industry is willing to put in the effort to make advancements. Semantic big data analytics and cognitive computing projects, which have foundations in NLP, are seeing significant investments in healthcare from some recognizable players. Allied Market Research has predicted that the cognitive computing market will be worth . The same company has projected spending of . Eventually, natural language processing tools might be able to bridge the gap between the insurmountable volume of data in healthcare generated every day and the limited cognitive capacity of the human brain. USD 13.7 billion across industries by 2020 USD 6.5 billion on text analytics by 2020 NLP has found applications in healthcare ranging from the most cutting-edge solutions in precision medicine applications to the simple job of coding a claim for reimbursement or billing. The technology has far and wide implications on the healthcare industry, should it be brought to fruition. However, the key to the success of introducing this technology will be to develop algorithms that are intelligent, accurate, and specific to ground-level issues in the industry. NLP will have to meet the dual goals of data extraction and data presentation so that patients can have an accurate record of their health in terms they can understand. If that happens, there are no bars to the improvement in physical efficiency we will witness within the healthcare space. In conclusion, natural language processing in healthcare is well and truly placed to bridge the gap between the massive amount of data being generated on a daily basis and the limited cognitive capacity of the human mind.  From the most cutting-edge applications to the simple task of coding a claim for billing and reimbursement, NLP in healthcare has unlimited potential to turn data and processes from burden to boon.

Top 6 Applications of Natural Language Processing in Healthcare

One day, Dr. Jerome Lettvin decided to poke JD with a stick. JD — which stood for ‘ ’ — was the name of his . Juvenile Delinquent son’s pet octopus As a world-renowned thought leader in Biology and Artificial Intelligence, Dr. Lettvin (MIT) was studying JD the octopus at the Stazione Zoologica in Naples, Italy. Why? In order to create the world’s first artificial brain, of course. The next morning I walk into the lab and JD is up on the edge of his 20 foot tank. I walk in and smack! … right in my face… — Dr. Lettvin ( ) Poet, Psychiatrist, Electrical Engineer, Neurophysiologist A fearlessly charismatic Professor, , agreeing with him on certain subjects (denouncing the prohibition on marijuana 36:53) and outright calling bullshit (40:52) on other subjects. he once famously debated Dr. Timothy Leary on the subject on LSD Dr. Lettvin was so charismatic he once made . Danny would go onto invent over and perform pioneering work with . Danny Hillis quit Neurobiology 300 patents Disney Imagineering Any Product Manager will tell you that . Assumptions Kill Companies The assumption has always been that the eye mainly senses light, whose local distribution is transmitted to the brain in a kind of copy by a mosaic of impulses… — Dr. Lettvin ( — Conor Myhrvoid (MIT)) In a Frog’s Eye In his shocking paper with McCulloch-Pitts & Maturana, Lettvin showed that the Frog’s Eye was organizing information BEFORE sending the information to the frog’s brain. We were laughed off the stage, literally, at the American Physiology Society in Atlantic City, where we tried to present it [What The Frog’s Eye Tells The Frog’s Brain],” ­Lettvin told his collaborator Luis ­Amador in 1986. Even after the paper was published in 1959, the findings met with considerable skepticism. One disgruntled scientist — fellow MIT researcher Walter Rosenblith — ”felt we were … liars, held a meeting on perception, visual perception, and didn’t invite us,” Lettvin told ­Amador. Another colleague circumvented the snub by taking conference attendees on an unannounced visit to his lab so they could see for themselves how the experiment was done. The visiting audience was convinced, and Rosenblith soon apologized. “And that,” Lettvin concluded, “was the time that we began to be taken seriously.” — Dr. Lettvin, In A Frog’s Eye (MIT) In fact, people still assume that the brain is the only information processor in the body. Biology is interesting because when you go back and start looking at structure, the structure of cells and things of this sort, there’s an amazing amount of work on it, and awfully good work, but smelling how things fit together is a different story. Now you’re talking about strategy, and strategy is a very different kind of thing: the strategy of operation. — Dr. Lettvin ( ) Poet, Psychiatrist, Electrical Engineer, Neurophysiologist The body’s processes information. Assumption: brain The body’s AND body’s work to process information. Truth: eye brain together What a Frog’s Eye Tells the Frog’s Brain proved that the Frog’s Eye was organizing information it sent the information to the Frog’s Brain. BEFORE The discovery that our brain wasn’t the only thing in the body doing the thinking forced scientists to and Artificial Intelligence, including Professor Lettvin. fundamentally rethink their assumptions about the brain Now look, I’m not saying there are not good physicists who work in astronomy, they’re wonderful, and their apparatus’s are extraordinary! But, oddly enough, a lot of that extraordinary apparatus was already invented by animals. There are animals that are star-guided. It’s surprising how intelligent most animals are. : The Eyes sends information to the brain. Standard thinking (thinking up down, back to front, end to beginning): The Eyes thinks about the information before sending the information to the brain. Janusian Thinking The brain is the information processor of the body. Thesis: The eye is the information processor of the body. Antithesis: The eye receives information, processes it, and then sends the processed information to the brain for additional processing. Synthesis: As we now know, an octopus has neurons in its brain AND its tentacles. Neurons are what the brain uses to think. Lettvin learned just how smart his son’s Octopus JD really was the day after he poked him. “People talk about animals not having a sense of humor, this octopus knew how to play practical jokes, and it was wonderful to watch. My octopus would play practical jokes: “What do you mean an octopus playing practical jokes?!” you say. But it’s true.” — Dr. Lettvin ( ) Poet, Psychiatrist, Electrical Engineer, Neurophysiologist While Lettvin didn’t create the world’s first artificial brain in Naples, he learned something he’d never forget after experimenting with JD. The next morning I walk into the lab and JD is up on the edge of his 20 foot tank. He had been waiting for me to come in. Jonathan is in stitches, and then Jonathan says, “Take a look!” and I turn around and there are splotches of water all around the region of where my head would be when I come in. The octopus had planned his revenge and he had been practicing ahead of time. That sounds like a ridiculous story, eh? Well, it isn’t….It’s a very different science that I teach. — Dr. Lettvin ( ) I walk in and smack! … right in my face… he let out a huge squirt of water! Poet, Psychiatrist, Electrical Engineer, Neurophysiologist _The safest general characterization of the European philosophical tradition is that it consists of a series of…_hackernoon.com The Invisible Hand Behind The Future of AI _With the bullies from school hot on his trail, a terrified 12 year-old Walter Pitts dashed into a silent Detroit Public…_hackernoon.com The Tragedy of Artificial Intelligence _Why Do Humans Execute Certain Actions over Other Actions?_medium.com Why Do Humans Execute Actions? _John Baez is a Mathematical Physicist. He works at the math department at U. C. Riverside in California, and also at…_hackernoon.com Just Dao It — S01E01 — Flying Butterfly Gods _One day, naked and alone in a small room, he began to become aware of what he later called “the last of the human…_hackernoon.com The Last of the Human Freedoms _“A mass in movement resists change of direction. So does the world oppose a new idea.” — Nikola Tesla_hackernoon.com Scaling the Office Romance _Calculus Made Easy_hackernoon.com I dare you to find me a more hilarious calculus textbook _Complexity Science is NOT a New Science._hackernoon.com Complexity Science is NOT a New Science Hi. My name’s Mike. I’m a s in Toronto. My purpose is to teach ideas worth teaching — no more, no less. Headhunter Launching Startup

The Man Who Called Bullshit on AI

As hot as stories about artificial intelligence (AI), augmented/virtual reality (AR/VR), blockchain , and the Internet of Things (IoT) have been in recent months, we often can’t help but think of these technologies as a long way off from mainstream adoption. Movies like Ready Player One and Avengers: Infinity Wars only perpetuate this perception by mixing real technologies with fantasy, making the tech we wield in the real world seem primitive in the process. The reality, though? AI, AR/VR, blockchain, and IoT are already playing an important role in our everyday lives, with countless examples hiding in plain sight. In Part One of our series, we look at Artificial Intelligence (AI) . Artificial Intelligence is NOT Killer Robots In pop culture, AI is often represented as a highly-evolved and self-aware artificial “brain” in a robot capable of overthrowing the human race, as in or . Ex Machina The Terminator movies In reality, AI is overwhelmingly beneficial to humans and is more often associated with software applications than with robots. And it’s already here, in applications we use every day. Mapping and Ridesharing Google Maps being able to predict your commute time or suggest the fastest possible way to work relies on , a form of AI which leverages the vastness of historical trip data to make predictions or decisions. So too does Uber or Uber Eats, when it tells you the estimated time it will take for your chow mein to arrive. machine learning (ML) What’s perhaps even more impressive is that Google is able to , and then use ML to keep Google Maps updated. use computer vision (CV), another form of artificial intelligence, to “read” street signs in images captured by its Street View cars Google Maps analyzes historical trip data to let you know how to go somewhere and how long it might take. Ecommerce and Customer Service By now, you’re probably aware that Amazon analyzes your shopping behavior on its website and shows you merchandise you’re the most likely to buy, based on past page browsing and purchase history. That’s ML/AI, too. So too is the Seattle tech giant’s Alexa, an AI assistant that can do everything from control your home appliances to help you order another box of diapers from Amazon. (How convenient!) It’s not just Amazon that’s investing heavily in AI assistants; Microsoft, Google, and Apple are a few others who have introduced such AI personalities to the world as Cortana, Google Assistant, and Siri, respectively. Machine learning algorithms are also being used to automate warehouse operations, allowing companies like Amazon to fulfill upon their lightning fast delivery expectations. Businesses that must provide customer service or support are now turning to AI-enabled chatbots, which are capable of interacting with customers without human input. These chatbots allow businesses to provide a level of service that most customers find acceptable for routine inquiries, without having to employ a large workforce of customer service reps. Dating & Social Networking The dating apps you (or your single friends) use are investing heavily into AI/machine-learning to provide even better matches for you to swipe left or right on. Tinder, for example, which uses AI-based matching to occasionally suggest four users it thinks you have a great chance to hit it off with, offering you a free “Super Like” to use on one of the four. (Using a “Super Like” increases a user’s likelihood of matching by three times, according to Tinder.) began testing a new feature last year called “Super Likeable,” Another ML feature in Tinder is the ability to have Tinder automatically use your best, most successful photos in an automated form of A/B testing. This is something a competing service, OKCupid, has implemented as well. When OKCupid users are not using their most effective photos, the app alerts them so they can change to a better photo. Dating apps (or even social networking apps, for that matter) that gain a mastery over machine learning algorithms will absolutely turn the industry on its head — imagine not having to swipe at all to meet your perfect match after your first month, because the app has learned exactly what you’re looking for. Tinder’s Super Likeable feature occasionally present you with four matches Tinder’s AI algorithms thinks are especially great for you. Advanced Analytics in Sports Are you a fan of the NBA? Computer vision (CV) and AI are becoming an important tool in every NBA team’s arsenal. Back in 2013, a company called SportsVu partnered with the NBA to provide six motion capture cameras in every NBA arena. The cameras generated data that was then able to be used to provide all kinds of machine learning-based observations that quickly proved superior to mere human observations. Today, a company called Second Spectrum has the rights to provide this rich motion capture data to every NBA team, and its services are a big reason why teams like the Golden State Warriors are all-in on “small ball” and three pointers. Major League Baseball (MLB) has been using artificial intelligence even longer than the NBA, as anyone who’s watched Moneyball probably knows. MLB teams use , or advanced in-game statistical data, to model game outcomes when using different lineups (such as pitchers), batting orders, or other tactics at a manager’s disposal. sabermetrics Music Recommendations Going back to consumer applications, if you’ve ever used Spotify’s , you’ve allowed (perhaps unwittingly) an AI to dictate your musical preferences. (Don’t feel bad, though; pretty soon, AI will tell us what to wear, eat, and do with our lives — and we’ll gladly listen!) “Discover Weekly” feature Spotify’s “Discover Weekly” uses machine learning to give you curated playlists of songs you “should” like. Avoiding Killer Robots Any responsible overview of artificial intelligence should introduce the idea of , the theoretical point at which super AI becomes capable of rapid self-improvement, such that it would no longer need humans and therefore could theoretically pursue our extinction. technological singularity So real is this possibility to some that Elon Musk, CEO of Tesla, has gone on the record to say: “With artificial intelligence, we are summoning the demon. You know all those stories where there’s the guy with the pentagram and the holy water and he’s like, yeah, he’s sure he can control the demon? Doesn’t work out.” However, most experts agree that this is a scenario we won’t need to worry about with proper governance and is not even likely to be in the cards until 2040–2050 at the earliest. (There’s no need to lock up your Roomba just yet.) Today and for the foreseeable future, AI is benign, pro-human, and is proving enormously valuable for companies that choose to implement it into their applications. “With regard to mobile apps, AI is one area in which we’re seeing massive interest. AI-enabled mobile apps are particularly interesting because anybody with a smartphone — even a 5 year old phone — running a well-developed app can tap into the wisdom of billions or trillions of data points from users who came before. That’s the beauty of cloud computing and AI,” explains Harry Lee, CEO of , a in Los Angeles and the San Francisco Bay Area. CitrusBits leading mobile app development agency In Part Two of our series, we’ll explore how Augmented Reality and Virtual Reality (AR/VR) are quickly becoming a modern reality and discuss some of the most interesting applications today.

“Future” Tech That’s Hiding in Plain Sight: Artificial Intelligence (Part 1 of 4)

A machine learning algorithm analyzes its weights and biases in the film Ex Machina Sometimes, when I jump in a self-driving Uber or notice that Facebook somehow tagged my profile picture with an ID of “stereotypical Colorado hiker,” I can’t help but wonder how close we really are to the dystopic robot uprising of or . Computers can play chess, but could they scheme, lie, or cheat? Can they be creative? Will they ever be able to critically analyze their place in the world and decide they want a better one? Ex Machina Westworld Recent breakthroughs rely on a strategy called to train networks of artificial “neurons” to perform specific tasks. These incredible algorithms have allowed for advances in every flashy tech pursuit from to , but they all rely on huge amounts of labeled data to evaluate and adjust their performance in the task at hand ( , for instance). machine learning supervised learning in-browser automatic translation control systems for self-driving cars classification Using supervised training to optimize a neural network for a specific task is great — if you only have one type of task to solve. And, even though some algorithms have been sufficiently generalized to apply to a few overlapping tasks, you can’t really count on a network designed for language processing to learn how to recognize images particularly well. This is a huge obstacle for the nightmare robot uprising: something about organic intelligence is just more fluid, more resilient to change, and more capable of forming subtle or unexpected associations. If we want to encourage the success of the nightmare robot uprising — which, for some reason, is the position of this article — we need to re-evaluate how we’re trying to build our artificial brain. We typically move from the top, which would be the specific result of a particular task, down through our network in order to teach it how to perform that task as well as it can. But a real brain forms in the opposite direction: it sends connections up through networks and, based on a few simple rules of attraction, encourages an ideal distribution of neuron clusters and pathways between them. This sort of is called , based on how huge colonies of insects (bees, for example) can work together to find optimal paths to food. The process relies on huge numbers of individual actors, each programmed with a few basic rules. A bee dances to communicate its findings with the hive, and the duration of a dance corresponds with the quality of the food source (in terms of quantity and proximity). Then follows a second generation of pilgrim bees, who are programmed to follow the path described by the very first dancing bee they encounter, and then to return home and dance about it themselves. Then a third generation does the same, and the process continues. emergent complexity swarm intelligence Eventually, the entire hive will find its way to the best food source, since the odds of encountering a bee dancing about that particular place will be slightly higher than the others initially (based on the length of the dance) and will continue to improve as more bees start twerking in that direction themselves. The entire hive, by virtue not of intelligence but of sheer numbers, can solve a problem together that no individual part of the group would be capable of solving on its own. Several bees teeming up to solve a difficult problem This is a lot like between new neurons: each neural “bee” (a ) has just a few rules it follows as it moves through clusters of neurons. Once enough of them move through the network, the basic rules governing their development allow for an optimal pattern of connections between the neurons to emerge. how a developing brain forms connections radial glial cell This approach to building a brain could make a big difference. In fact, when comparing the human brain with that of a chimpanzee, it appears that possibly the is simply the quantity of neurons and their connections — I mean, the number of bees in the hive. The same emergent patterns that allow a chimp to fish for termites with a stick, in greater numbers, make us capable of understanding language. If only their brains could continue to develop as many new connections as ours can, they could presumably learn to do any of the amazing things that make us human (e.g. operate a Twitter account, online banking, etc.). single most important distinction All this brings us to a critical tool in developing a robust and flexible network for our terrifying army of artificially intelligent super-robots: Unsupervised models aren’t as well developed yet as supervised ones, but they simply use unlabeled data for training. They could conceivably allow for elegant patterns to emerge organically — if we can determine the best rules to instruct our dancing bees follow in the first place. unsupervised learning . We could also start to wildly speculate about building a hybrid solution by wrapping a series of neural networks, themselves pre-trained in a supervised setting, in an unsupervised model. The unsupervised container could send input through each pre-trained network and discover which network should solve what problems, which networks should be multiplied or mitigated, and where neurons might form long-range connections with other networks to provide more humanesque capabilities for creative association. This could allow for an artificially intelligent program that intuitively knows to analyze an image when it sees it, and may, by doing so, be reminded of a relevant song, and could, after thinking of that song, tell you about its whole train of thought in conversational English. Making these systems efficient enough for a pseudo-conscious intelligence to emerge is still an , even if the entirety of the network doesn’t have to be explicitly programmed by hand. But it’s important to indulge the _Westworld-_ian fantasies of feeling-bots in real terms every once in a while, if only so the study of machine learning and doesn’t get swallowed up by the ever-present buzzkill of commercial viability. incredible task artificial intelligence

How “Real” is Artificial Intelligence?

Imagine to be able to execute amazing tasks in some impossible places, like working on the surface of the moon in cyborging devices assembled there and experiencing looking to earth, will that be possible? If we try to make an observation about the changes in our advanced technology, we tend to feel that it will not be that far before witnessing new business coming to reality, business that was only possible in SF. I like the idea of cyborg and I asked myself how a start-up for “cyborging” will be like? Inspired by the “Singularity is Near — ” that I consider the bible for Technological Singularity, I imagined how would be a start-up in cyborging. Ray Kurzweil Like any product, your cyborg product require a story, basically you have to talk about your product what is the purpose, what do you expect it to achieve, this in a good length shouldn’t be more than a short paragraph. This product require some honest questions and answers from your side, you have to be honest with yourself: What are my intentions with this product? What will be the profit? (I don’t know what will happen to the concept of Money, the concept of money is bound with the individuality of humans, it will stay for sometimes until it is replaced with something else) Regulations Knowing the fact that cyborg technology involve extension of human being’s physical and cognitive abilities, that connects and merges directly with the biological side, raises important ethical considerations and concerns, and also have implications on the view of human nature. I will not be surprise to see that very strong lobby of anti-cyborg will come and the only way to win is to push always upfront the benefits of the new concept or technology, the world of business will be a combination of strong alliance of technology-business lobbies that have no geographical borders and I will not be pessimistic if I said investing inside such atmosphere will be not easy, we may solve issues regarding environment but we will not be able to solve issue related to so called guardians of ethics against the avant-guard of business-technology. Investors I imagine in 50 years time it is possible that we witness a major change in the way economy works, venturing will be based on much more calculated process that run based on “Oracle prophetic algorithms”, by that time investors have no gut feeling anymore, it is all about analytic and speculation of big data of market and customers trend, expecting trends before they happen, there will be no shortage of finance, but maybe a lot trend manipulation, all will be about the creative mind and meetings demands and expectation of a much more hungry consumers, appetite for goods will rise, a new appetite based on enjoying the new consumerism experience, consuming virtual goods, feeling the data. Hiring By the time we reach the level where investing in cyborg, new skills will appear and new employee may come to the market; the artificial brain, customized brain (processing system if you want me to make things easy) I believe that will be the new skills, individuals hiring their artificial brain to more than one place at the same time with no conflict of interests, basically it is a system that is in the could, propriety of the individual much more like a server but it is not, an investment where a person feed the brain with necessary process and data that will enable to brain to perfect specific process in a specific domain. the person will be free from the contact to the management, he or she are just responsible-owner of the hired skills residing in the artificial brain. The making The easy part of the making of a cyborg is to stop thinking of a cyborg like a replacement to the body, cyborging will be an inject the volunteer with billions of neurobots as i like to call them (nanobots), track them by holographic mapping until they reach the brain of the consumer, start to pair the the signatures of the neurobots, by the end a mapping of the brain will be processed by application determine the full connection of the consumer and the next part which to come: The machine. The final result In 50 years time the distance between man and machine will be short to the point to think that it is part of the human life, what ever we created was always an extension of us, we will start to experience a whole workforce of machine that achieve tasks remotely controlled by a mix operator of bio-crowd-computing system and non-biological-brain monitored by biological “brains” that could be artificially “born”, you could imagine anything, any device, the aim is the preserve the human continuation or maybe that is life. When you reach this line, you look how naive this article, but all dreams are, until they become reality and turn to nightmares which are the unexpected variables that fall from equations, and those variables that will lead to dream of a better horizon.

Stories, News & Mentions about ARTIFICIALBRAIN

Artificialbrain’s Stories on HackerNoon