Short & Sweet AI

Hi, I’m Dr. Peper and today I’m talking about ImageNet. The story of AI is the story of the pioneers who created it and ImageNet is about a brilliant AI researcher by the name of Fei Fei Li. As always, the links for further reading, videos, and podcasts for this episode are in the show notes.

Fei Fei Lee is considered the rock star of computer vision and articles about her say she started the deep learning revolution and changed everything. As a freshly graduated computer scientist at Princeton, she came up with a revolutionary approach to teaching computers how to recognize images. At the time, scientists were writing computer code, also known as algorithms, to identify cats and then a different algorithm to identify dogs and so on for each object. She thought this was too narrrow. She thought it should be more like how a child learns to recognize images. Children learn to recognize by looking at millions of images. Then she had a brilliant idea: it’s not about the algorithms but the data that you gave the algorithm. So she began to focus on creating datasets.

Datasets

The idea of creating a data base to train computer algorithms to recognize images was considered so ludicrous, laborious and expensive that she couldn’t get funding. In fact an NIH comment rejecting her grant application stated it was shameful Princeton would research the topic. At first she paid undergraduate students $10 an hour to label images but quickly realized at that the slow pace, it would take nine years to create the data set and so the project stalled and languished until a chance conversation with someone who suggested she look at Amazon’s Mechanical Turks. The Mechanical Turks is a system of workers worldwide being paid very small amounts to do piecemeal work. This was a breakthrough for hiring a cheap, fast labor force to label the images. Even so it took another two and a half years to amass the initial 3.2 million images called ImageNet.

Li and her team then offered their dataset to an image recognition contest. In the competition, AI researchers would use their newly developed algorithms to see how accurately they could identify the images in ImageNet. In the beginning the best algorithms in the contest could identify the images with only 75% accuracy. Then in 2012 something very big happened. Researchers won the contest using a type of deep learning algorithm called a https://drpepermd.com/episode/4-are-machine-learning-and-deep-learning-the-same-as-ai/ (convolutional neural network) with amazing accuracy. And each year after that the neural networks improved until the accuracy was 98%. In effect computers could see better than humans.

Data = Fuel

The 2012 event triggered a wave of excitement. There was a huge acceleration in using deep learning and convolutional neural networks which launched a https://drpepermd.com/episode/1-three-breakthroughs-unleashing-ai/ (revolution). ImageNet changed the field as people realized the thankless task of making a dataset was at the core of AI research. It wasn’t just about the algorithm or neural networks.

Today ImageNet has 15 million labelled images and large companies such as Google and Facebook have created their own datasets of voice clips, text snippets, even video datasets of people performing tasks. Datasets are the fuel for the different deep learning neural networks which have ushered in new technologies such as advanced smart phone cameras and self-driving cars.

And it all started with Fei Fei Li and her quest to teach machines to see.

However, as with all technology, there are unforeseen consequences, the unknowable unknowns. And in my next talk we’ll see how ImageNet has become the poster child of what bias in AI l...

Hi, I’m Dr. Peper and today I’m discussing fake radiology.

A person’s health information is considered so sensitive and private Congress enacted the Health Information Portability and Accountability Act or HIPAA, to ensure each personal’s medical information is safe. Hospitals are very careful with sharing medical data with outside doctors or other hospitals. But what about the privacy and security of a patient’s medical data within the hospital system? What if patient’s medical records, tests, even CT scans were vulnerable to manipulation from malicious software viruses in the hospitals digital system? A group of researchers from the Cyber Security Research Center in Israel wanted to show how the power of AI deep learning could be used to add or remove medical conditions from CT and MRI scans and cause a patient to falsely believe they have a serious illness.

CT Shows Fake Tumor

They showed how this could be accomplished in a real setting and published a paper about the results. Here’s what happened. After a hospital gave the researchers permission, they remotely inserted a malware virus into a hospital’s radiology network of CT scans and MRI scans. Real lung CT scans were altered by the malware to show fake lung tumors in normal scans and remove real tumors in scans that showed disease. This was serious stuff. As a result, the radiologists reading the scans were tricked into misdiagnosing lung cancer in most of them. Radiologists read the scans as showing cancer 99% of the time when a fake tumor was added to a normal scan. And when a real tumor was removed using the malware, the radiologist said the patient was healthy 94% of the time.

What is even more disturbing about this is most hospitals use AI powered lung scanning software tools to aid the radiologist in detecting tumors and confirm their diagnosis, but in this study, the malware was able to trick the CT software scanning tools into confirming the fake tumors every time.

Hospitals Need Encryption Within

The study sent shock waves through the hospital and medical community as authorities realized they need to encrypt their network system not only from the outside but from within. Hospital officials were quick to note that controls exist to prevent a patient from receiving unwarranted treatment. And there are several steps before a patient goes to surgery or receives radiation or chemotherapy so that a fake result would likely be detected. But there is emotional harm to the patient and the distress of learning they may have cancer even though it’s subsequently proven to not be true.

Fake Illness + Politics

And in truth, the cybersecurity researchers were thinking of another type of harm when they staged the attack. They wanted to draw attention to the weaknesses in the medical imaging networks to potentially avoid another type of ominous scenario, one that could affect our political system and government. They worry that attackers using this malware could target a presidential candidate or other politicians to trick them into believing they have a serious illness and cause them to withdraw from a race to seek treatment.

I hope this helps you to better understand the real threats of artificial intelligence.

The specific article and further readings, videos, and other podcasts are linked in the show notes.

From Short and Sweet AI, I’m Dr. Peper.

https://www.washingtonpost.com/technology/2019/04/03/hospital-viruses-fake-cancerous-nodes-ct-scans-created-by-malware...

In my previous flashes I talked about how DeepMind’s AlphaGo beat the world’s best human Go player by using reinforcement learning and deep learning and giving the computer lots of games to analyze and learn from. But what if the computer system had to learn entirely from itself? What if it’s given no human knowledge but had to learn from scratch?

To answer that question DeepMind experts created AlphaZero a single system which taught itself how to master the games of chess, shogi (Japanese chess) and Go. AlphaZero was given the rules for each game and then through random play, and with no built in human knowledge, learned by playing against itself millions of times. Initially it’s games were weak and erratic but over time it learned which game strategies worked and were successful. It learned a pattern that caused it to win a game and used that pattern more and more and patterns that lead to losing were used less and less so that the system was more likely over time to choose more advantageous moves.

AlphaZero ultimately defeated AlphaGo, the world’s best Go player, 100 games to 0. Researchers realized that when you put your preferences and predispositions into the computer system, it made the system weaker. The system that learns from itself is a stronger player. By playing 44 millions games against itself, in 2019 AlphaZero had become the best player in the world for Go and shogi. And Alpha Zero became the best chess player in the world with astonishing speed. The headlines read “Entire human chess knowledge learned and surpassed by DeepMinds Alpha Zero in 4 hours.” The byline was that it was essentially managed in little more than the time between breakfast and lunch.

However the most fascinating part about AlphaZero’s abiliites was the style used by the computer system to win at these games. Being self taught, AlphaZero didn’t follow conventional wisdom of the games but developed it’s own intuition and strategies that were completely novel and never seen before. World champion players described the game playing as ground breaking and highly dynamic. For example in chess, AlphaZero de-emphasized the importance of each piece’s value, sacrificing highly valued pieces early on for an advantage in the game in the long term. In a new book about AlphaZero’s chess games called Game Changer, the authors state, “It’s like discovering the secret notebooks of some great player from the past.”

AlphaZero’s ability to master and become world champion of 3 different complex games demonstrates a self teaching system can work for any information game but more importantly, can discover new knowledge in a range of settings. This brings DeepMind closer to it’s ultimate mission to solve intelligence by creating general learning systems, in essence, artificial general intelligence, and then using that to solve all the other world problems.

A transcript of this and other podflashes, along with additional reading, can be found at my website, drpepermd.com.

From short and sweet AI, I’m Dr. Peper.

https://drpepermd.com/wp-content/uploads/2020/01/19-AlphaZero.docx (#19 AlphaZero Download transcript here )

https://deepmind.com/blog/article/alphazero-shedding-new-light-grand-games-chess-shogi-and-go (https://deepmind.com/blog/article/alphazero-shedding-new-light-grand-games-chess-shogi-and-go)

https://www.newyorker.com/science/elements/how-the-artificial-intelligence-program-alphazero-mastered-its-games (https://www.newyorker.com/science/elements/how-the-artificial-intelligence-program-alphazero-mastered-its-games)

https://deepmind.com/blog/article/podcast-episode-2-go-to-zero (https://deepmind.com/blog/article/podcast-episode-2-go-to-zero)

In 1997 a chess playing computer built by IBM called Deep Blue beat the world chess champion Gary Kasparov. You may wonder why AI researchers are so interested in building a computer system to beat human level games. It’s because it’s a way to test a computer’s abilities and drive a new kind of research that could lead to the next big breakthrough in artificial intelligence. Games are a testbed for AI.

That next challenge was Go, an ancient Chinese board game considered to be the most popular game in the world, taught in Chinese schools alongside math. Go is relatively unknown in the Western world but it’s considered to be perhaps the most complex game ever devised by humans. In chess, a player has about 35 possible moves to choose from in a given turn, in Go, it’s around 200. Chess can be thought as a metaphor for a battle. Go is more like a geopolitical war where a move in one corner of the board can ripple everywhere else. The result is that Go players can’t look ahead to the ultimate outcome of each contemplated move, like in chess. The top players use intuition and follow a type of aesthetic which has made it a fascinating game for thousands of years.

Experts at DeepMind got to work creating a computer system known as AlphaGo. David Silver, the lead researcher, began with reinforcement learning algorithms but realized something was missing and combined reinforcement learning with deep learning which had deep layered representations of knowledge known as neural networks. This combination created major AlphaGo breakthroughs.

In 2016 in a televised event with a 100 million people watching, the world’s best Go player, Lee Sedol from South Korea, played the AlphaGo computer system in five games. Lee Sedol had been Go world champion 18 times and Demis Hassabis, the co founder of DeepMind, explained the match pushed AlphaGo to it’s limits.

In one moment in the second game, the audience was transfixed and horrified when AlphaGo made a surprising, unexpected move, now made legendary and referred to as Move 37. It was a move that went against all conventional wisdom used in playing the game. AlphaGo had created a new pattern of playing and came up with a long shot, a move that showed an insight beyond what even the best players could see. The move was later described by Go players as showing intuition and something totally original, it was described as a move of beauty.

Lee Sedol rallied and in game 4 he placed the 78th stone on the board in between 2 of Alpha Go’s stones. It’s called a wedge move and it was brilliant and took AlphaGo by surprise, everything it had done up to that point was rendered useless. AlphaGo ultimately lost the game. Like a human, the machine had blind spots. That move was dubbed God’s Touch and although Lee won that game, in the end, Alpha Go prevailed winning 4 games to 1.

This was a revolutionary accomplishment for a computer system to beat the world’s best Go player and a decade earlier than expected. The world was stunned. First there was sadness that a computer could beat a Go hero. But then there was another emotion, one of excitement that human players could see more possibilities now in playing the game. Lee Sedol said playing against Alpha Go brought him renewed joy in playing and improved his skills and abilities in a way that playing against other human players had not. He went on to win over a 100 games in a row against human players. In 2017 AlphaGo beat the number one world Go player Kie Je from China and after that DeepMind retired AlphaGo while continuing research in other areas.

But interestingly, AlphaGo’s win against Lee Sedol in 2016 was a turning point in China. The Chinese government experienced a “sputnik moment” which convinced them they needed to prioritize and dramatically increase funding for artificial intelligence. The race between the US and China for AI superiority was on.

From short and sweet AI, I’m Dr. Peper.

DeepMind is the world’s largest and most prestigious company focused on artificial intelligence and really came into the public eye in 2016 when it beat one of the world’s top players in the game of Go, a Chinese game that is more than 4000 years old. That was a breakthrough in AI and came a decade earlier than many experts had predicted.

DeepMind’s been owned by Google since 2014 but was started by Demis Hassabis, who some have described as the brains behind DeepMind. In 2010 he, along with 2 friends, Shane Legg, and Mustafa Suleyman cofounded DeepMind in London, with the ambition to solve intelligence and then use that to solve everything else.

One thing to know about DeepMind is it uses something called reinforcement learning or RL, which is a type of dynamic programming that trains algorithms by using a system of reward and punishment. A reinforcement learning algorithm, also called an agent, learns by interacting with its environment. RL is considered by some to be the future of machine learning.

DeepMind is focused on finding the holy grail of AI which is artificial general intelligence or AGI. Demis Hassabis defines artificial general intelligence as a system capable of solving a whole spectrum of cognitive tasks on a level that is at least as good as humans are able to do.

So what is Google doing with DeepMind? At Google, DeepMind has continued research into artificial general intelligence while the DeepMind AI has been broadly integrated into Google products and services in areas of speech recognition, image recognition, fraud detection, identifying spam, handwriting recognition, translation and of course, local search.

Two notable areas where DeepMind has made an impressive impact is crazily enough the medical field and the world of video gaming.

In medicine, DeepMind has applied its abilities to protein folding with an accelerated understanding that has astounded eminent researchers. Protein folding is the process by which chains of protein building blocks fold over each other to form 3D structures. Many diseases such as Alzheimer’s and Parkinson’s, are thought to be caused by proteins misfolding and being able to predict the structure of proteins that cause these diseases could lead to more specific drugs to treat them.

Perhaps the most significant accomplishment to date has been that DeepMind has figured out how to beat humans, not only in the landmark win of the game Go, but more recently in 2019 it performed on a level equal to humans to win in a version of capture the flag. DeepMind also showed it was capable of teaming up with both artificial agents (which are the reinforcement learning algorithms I mentioned before), so it was able to team up with other AIs as well as human players to defeat its’ opponents. This is a significant achievement showing that DeepMind can strategically out-think humans. Others have deep concerns it may represent a first step in the Rise of the Machines.

So if artificial general intelligence is the holy grail, how will we know we’ve achieved it? If you ask Demis Hassabis, he says that big moment will be when an AI system comes up with a completely new scientific discovery that’s of Nobel prize winning level. Will it be DeepMind accepting the 2045 Nobel prize in Medicine or maybe Military?

From short and sweet AI, I’m Dr. Peper.

https://drpepermd.com/wp-content/uploads/2020/01/17-Deepmind-Gaming-and-the-Nobel-Prize-.docx (#17 Deepmind, Gaming and the Nobel Prize download transcript here)

https://www.techrepublic.com/article/google-deepmind-the-smart-persons-guide/ (https://www.techrepublic.com/article/google-deepmind-the-smart-persons-guide/)

https://www.techworld.com/startups/google-deepmind-what-is-it-how-it-works-should-you-be-scared-3615354/ (https://www.techworld.com/startups/google-deepmind-what-is-it-how-it-works-should-you-be-scared-3615354/)

AI and medicine…where to begin? There is so much going on in how AI is impacting healthcare. It’s a meta trend that’s been developing over the last 20 years. I’m going to highlight 5 areas where AI is driving big changes.

The first technology is the brain computer interface or BCI, which you’ve heard me discuss before in my episode on brain hacking. BCIs are direct connections between computers and the human brain being used to restore function for patients who’ve lost the ability to speak or move or interact with their surroundings. This includes people suffering from ALS, strokes, or the 500,000 victims who have spinal cord injuries every year.

The second disruptive technology is AI radiology tools. Deep learning use neural networks, remember my discussion of them in my episode on deep learning and machine learning, Neural networks work in a way inspired by how the human brain processes information through many layers. Computers using neural networks have already proved their ability to match or exceed the accuracy of human experts when analyzing images. For example there’s a FDA approved AI program embedded in a mobile x-ray machine to identify and prioritize collapsed lungs on STAT x-rays. About 60% of all x-rays in a hospital are marked STAT. This AI enhanced x-ray machine flags images with possible pneumothorax (which is a collapsed lung) so those x-rays get looked at by a radiologist first, speeding up diagnosis and getting care to patients who are most ill.

Similar machine learning systems are used for more accurate detection of diseases from all types of imaging studies like CAT scans, MRIs, mammograms and even everyday detection of broken bones on x-rays.

A third impact is AI is being used in some cases to drive down to the pixel level of tissue biopsies seen under the microscope, thereby detecting changes not routinely observed by the doctors reading them. This is called digital pathology and is important because 70% of all decisions in healthcare are based a pathology result. The more accurate the image, the faster the right diagnosis is made and treatment can begin.

The 4th innovation is harnessing the power of smart phones using their great camera quality. Smart phone photos are analyzed by AI algorithms to diagnose skin cancer and eye diseases. And there are many other phone uses. So far there is a disposable sensor that plugs into a smart phone and can diagnose HIV, a glass ball that attaches to the smart phone camera that turns it into a microscope to detect malaria, and two clinical trials underway. One trial is testing a smart phone app that can diagnose acute respiratory problems in children by analyzing their cough and another trial is investigating the use of FitBits to collect data to diagnose Parkinson’s disease.

The fifth area is using AI to get ahead of chronic disease and could be where AI makes the biggest impact in the healthcare system. Clinically validated machine learning algorithms are being used to generate a patient’s risk for congestive heart failure, macular degeneration, aortic aneurysm and even hospital readmission based on medical data from their charts. Knowing which patients are at risk can lead to earlier interventions and even changes in their current treatment. In this way, AI generated clinical decisions using lots of patient data can make doctors more aware of the nuances in a patient’s health to get ahead of any developing medical problems.

These are 5 of the many, many ongoing impacts AI is making in healthcare. As a doctor it’s overwhelming to me just the medical applications of AI, and makes me even more committed to discuss AI in way everyone can understand, in a way that’s short and sweet. I’m Dr. Peper.

https://drpepermd.com/wp-content/uploads/2020/01/AI-and-Medicine.docx (#16 AI and Medicine Download Transcript Here)

Tomorrow is January 1, 2020 and we do not just start a new year but a new decade. In the world of artificial intelligence, some believe in this decade we will pass the Turing test. What is the Turing test and why is it important?

The Turing test is a measure of the power of Artificial Intelligence. When a computer passes the Turing test, it means it will be equal to humans in every way. The test was developed by the pioneering computer scientist Alan Turing in 1950 to determine whether a machine has human like intelligence. The machine passes the test if a interviewer cannot tell whether a response is coming from the machine or a human. If someone can’t tell the difference then we consider AI to be of human intelligence. In 1999 the futurist Ray Kurzweil predicted machines will pass the Turing test by 2029. That is this decade.

So how does the Turing test work? In his paper entitled Computing Machinery and Intelligence, Turing outlined a method for answering the question “can machines think.” He proposed a hypothetical game with 3 players. One player is a interviewer separated from the other 2 players, one of which is a computer and the other a human. So you have a human interviewer asking questions of a computer and another human. Through this process the interviewer tries to figure out which is the computer and which is the human. Ultimately if it can’t be determined which one is a computer, then maybe they’re dealing with a thinking machine that has passed the Turing test.

Is it realistic to anticipate human level machine intelligence by 2029? AI researchers believe we have the computational power to build Turing’s thinking machine but a major problem is that computers still struggle with routine small talk and are even much worse than me at telling jokes. Language is widely seen as humankind’s most distinguishing trait. And for a machine to have a conversation with a person takes more than increasing memory and processing power. It requires understanding the meaning of language and all the implications in speech.

Kurzweil, head of natural language at Google, is more concerned that when the machine passes the Turing test, we’ll have to be careful about what kind of feelings that computer has, about it’s emotions and consciousness and we’ll have to care about what its’ thoughts are. He thinks future AI is emotion and will come with the Turing test being passed. And although consciousness is a philosophical question not a scientific question, because you can’t test for it, he believes computers will be conscious and have all the secondary features we associate with consciousness such as having an opinion, an ego, and desires. And that raises questions about what it means to be human.

As an aside, in my reading for this flash talk, I came across a comment which raises a subject I want to discuss in 2020, something I call dystopian AI and encompasses the ethics of artificial intelligence. The comment was: “ I’m not scared of a computer passing the Turing test. I’m terrified of one that intentionally fails it.”

https://drpepermd.com/wp-content/uploads/2019/12/15-The-Turing-Test-2029.docx (#15 The Turing Test 2029 Download Transcript Here)

From Short and Sweet AI, I’m Dr. Peper

https://www.abundance.video/videos/ray-kurzweil-peter-diamandis (https://www.abundance.video/videos/ray-kurzweil-peter-diamandis)

https://www.wired.com/story/ray-kurzweil-on-turing-tests-brain-extenders-and-ai-ethics/ (https://www.wired.com/story/ray-kurzweil-on-turing-tests-brain-extenders-and-ai-ethics/)

https://en.wikipedia.org/wiki/Turing_test (https://en.wikipedia.org/wiki/Turing_test)

https://www.economist.

We hear so many different estimates of how long before we see them on the road, but where are the self-driving cars? I’m using the term self-driving to mean a driverless car capable of navigating, avoiding obstacles, and parking without any human involved. And here it’s important to make the distinction between autonomous cars which have a driver at the wheel so they’re not driverless, and truly self-driving cars that don’t need a human operator or even a steering wheel.

Making a Care Autonomous

A car becomes autonomous by having AI software trained on virtual cars. The car drives billions of miles with every conceivable obstacle and situation thrown at it to see how it responds and uses deep learning algorithms to teach itself what actions lead to crashes. This way the car slowly learns how it should drive on real roads. Only through using AI can the car then go out on a real road to drive.

The Safety Challenge

An obvious reason for the ongoing delay of autonomous cars on the road is safety. In 2018 an autonomous car being tested by Uber hit and killed a woman walking a bicycle across the street in Arizona even though the car had a driver at the wheel and elsewhere in the US, 3 Tesla drivers have died in crashes when the drivers and the autopilot failed to detect and react to road hazards. While 80% of the technology exists to put self-driving cars into routine use, the remaining 20% is much more difficult because the AI software still needs to improve to the point where the cars can reliably anticipate what other drivers, pedestrians, even cyclists will do and navigate the unexpected situations.

The Standardization Challenge

The second challenge is more regulatory. Standard definitions are needed for what constitutes reasonable actions taken by the car such as how fast to drive or when to change lanes. All autonomous cars have been programmed with algorithms for speed and lane change but these algorithms need to be standardized for the industry so that automakers can program their cars to act only within those bounds. This also gives a legal framework for evaluating blame in accidents based on whether the car’s decision-making system followed the accepted standards.

Governments are moving to create standards and then approve not just autonomous but self-driving cars for use on a national level. There’s many concerns about the accidents and AI malfunctions. But even more troubling is the concern about malicious AI attacks by hackers, who could, for example, infiltrate the artificial intelligence system of a fleet of self-driving cars and cause them to ignore safety laws. Researchers at a watchdog group called Open AI and whose members include Elon Musk, Max Tegmark and others concerned about responsible AI have called for companies to work with each other and with lawmakers to safe guard against potential vulnerabilities to hacking. But will rivals such as Uber, Waymo, and Tesla be willing to share data for the safety of all in such an intensely competitive market?

Autonomous Vehicles in Use Today

Surprisingly, autonomous vehicles are actually in use today. A company called May Mobility operates autonomous, six passenger golf carts in 3 cities, driving short defined routes at 25 mph and the Brooklyn Navy Yard will have 25 mph driverless shuttles in use this year (fyi my daughter’s workshop is in the Brooklyn Navy Yard so I’ll have to go check it out). At low speeds in defined routes, autonomous vehicles are safer so the technology can be used today.

Getting back to the question of when will autonomous cars be on the road? Two automakers, Ford and Volkswagon have teamed up with an AI company and predict they will have ride sharing services in a few urban areas as early as 2021. Elon Musk, ever the optimist, has said “I’d be shocked if it’s not next year at the latest.”

Gradually, and then suddenly

From short and sweet AI, I’m Dr. Peper, and today I’m talking about The Singularity is Near.

In my research for the my flash show on Cyborgs, I came across Dion Dalton Bridge’s interesting article in which she referenced a quote from The Sun Also Rises where a character is asked, how did you go bankrupt? And he responds, “ Two ways. Gradually, then suddenly. “ It’s a highly appropriate description of the current technological tsunami taking place previously described as The Singularity is Near.

Ray Kurzweil is probably the world’s foremost futurist and has written several books about AI and intelligent machines but the one that has hit home perhaps the most is The Singularity is Near. He emphasizes that technology is accelerating at an exponential rate which means that in this century we will not experience 100 years of progress but more like 20,00 years of progress.

He presents the singularity as the moment during this time when human intelligence merges with artificial intelligence and vastly enhance our human capabilities. The word singularity is taken from the mathematical term referring to a value that does not have a finite limitation. So with the Singularity, human intelligence augmented by AI will no longer be limited but can accomplish the infinite. As he says in his book “The Singularity will represent the culmination of the merger of our biological thinking and existence with our technology, resulting in a world that is still human but transcends our biological roots.”

Needless to say Ray Kurzwel is a transhumanist and some consider the book to be the transhumanist manifesto. But what are some of the specifics of what happens?

Well nanotechnology plays a big role with robots the size of red blood cells inserted in the body augmenting or replacing our major organ systems and allowing the complete scanning of the brain to create a hybrid intelligence previously unknown. Ultimately, he predicts, human intelligence will be mainly non- biological and more of our experiences will take place in virtual reality than in the physical world. This includes having a “back-up” of our consciousness if needed, never getting sick and most importantly, we will never have to die.

His is a radically optimistic and genuinely inspiring vision of the future course of human development but raises many concerns over loss of jobs, increasing inequality, and who decides how we use this technology. Yet one thing to remember is that of 147 predictions Kurzweil has made since the 1990s, fully 115 have turned out to be correct, that’s an 86% accuracy rate.

And BTW, how near is the singularity? In the book, Kurzweil says 2045.

https://drpepermd.com/wp-content/uploads/2020/01/Gradually-then-suddenly....docx (#13 Gradually, then suddenly. The Singularity Download transcript here..)

https://www.kirkusreviews.com/book-reviews/ray-kurzweil/the-singularity-is-near/ (https://www.kirkusreviews.com/book-reviews/ray-kurzweil/the-singularity-is-near/)

https://www.itweb.co.za/content/dgp45vaG8p5MX9l8 (https://www.itweb.co.za/content/dgp45vaG8p5MX9l8)

https://electronics.howstuffworks.com/gadgets/high-tech-gadgets/technological-singularity.htm (https://electronics.howstuffworks.com/gadgets/high-tech-gadgets/technological-singularity.htm)

Recently my interest in cyborgs lead me to watch Alita: Battle Angel, a movie that presents the world of 2563 as being full of cyborgs with varying remnants of humanity. It’s takes place far in the future but in reality cyborgs are already among us and as some futurists tell us, we better get used to it.

Cyborg is short for cybernetic organism and is variously defined as a person whose function is aided by a mechanical or electronic device but a better version is much wider and could be any human relying consistently on some kind of technology.

In 1998 Kevin Warkwick implanted a device into his forearm and linked it to a computer to become the world’s first cyborg. Neil Harbisson was born with a severe colorblindness so he had a chip implanted in his brain which connects to an antenna that translates color into sound. The antenna curves up and over the back of his skull to dangle in front of his forehead making him look like a movie version of a cyborg. Another person had a sensor implanted in her elbow that vibrates whenever an earthquake occurs.

Five paraplegics with implanted spinal electrodes have been able to regain some movement. And a bionic eye systems made up of a camera attached to glasses and connected to a chip in the retina allows the blind to see and read letters again. There are millions of people who have augmented their bodies with cochlear implants to hear, cardiac pacemakers and implantable defibrillators to prevent sudden death and contact lens to improve daily vision.

The ultimate human – machine connection could be something called neural lace an emerging technology I mentioned in my flash briefing on brain hacking. Neural lace is a lace like electronic mesh that is injected into the brain to create a digital layer that sits above the brain and connects to the cloud thus giving access to all its’ stored information. And don’t we do this to some degree already with an external device by our constant interaction with our smartphones.

Lastly there is a global social and philosophical movement called transhumanism which advocates for the use of technology and science to enhance human intellect and abilities.

The lines between humans and machines are blurring everyday.

Maybe Elon Musk expressed it best when he said “We’re already a cyborg” .

From short and sweet AI, I’m Dr. Peper.

https://drpepermd.com/wp-content/uploads/2019/12/Cyborgs-Among-Us.docx (#12 Cyborgs Among Us Download transcript here )

https://www.forbes.com/sites/charlestowersclark/2018/10/01/cyborgs-are-here-and-youd-better-get-used-to-it/#582b5586746a (https://www.forbes.com/sites/charlestowersclark/2018/10/01/cyborgs-are-here-and-youd-better-get-used-to-it/#582b5586746a)

https://hackernoon.com/and-then-we-were-cyborgs-d56abc61442d (https://hackernoon.com/and-then-we-were-cyborgs-d56abc61442d)

https://www.theguardian.com/technology/2017/feb/15/elon-musk-cyborgs-robots-artificial-intelligence-is-he-right (https://www.theguardian.com › technology › feb › elon-musk-cyborgs-rob…)

https://www.pbs.org/video/scitech-now-present-and-future-cyborgs/ (The future of cyborgs and human augmentation | SciTech Now …)

https://www.pbs.org/video/scitech-now-present-and-future-cyborgs/ (https://www.pbs.org › video › scitech-now-present-and-future-cyborgs)

https://www.youtube.com/watch?v=LUd4qv2Qr0A (Cyborgs: A Personal Story | Kevin Warwick – YouTube)

https://www.youtube.com/watch?v=LUd4qv2Qr0A (https://www.youtube.com › watch)