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AI (artificial intelligence) is the simulation of human intelligence processes by machines, especially computer systems.

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Squirrel AI Learning by Yixue Group attends the hottest technology event of North America: Collision Tech Conference

TORONTO, July 15, 2019 /PRNewswire/ -- From May 20 to 23, the 2019 "Collision Tech Conference" was held in Toronto, Canada. As a cutting-edge technology conference with the fastest development in the North America, the event originated from Las Vegas and has been held six times this year. Nearly 26,000 participants, 3,750-odd CEO and 900-odd news media from 125 countries gathered in Toronto to discuss with science and technology leaders today's hot issues of science and technology. Derek Haoyang Li, the founder and chief education technology scientist of Squirrel AI Learning by Yixue Group, an AI education company from China, was invited to the Collision Summit to share the scientific innovation achievements of their respective fields with representatives of Samsung, Micro-soft, Y Combinator and stars of show business. Derek Li: Change education with AI "Science and technology have changed every industry, but education has remained unchanged. I wish to change education thoroughly with AI," Derek Li said at Collision. Squirrel AI Learning, established in Shanghai in 2014, is the first artificial intelligence company focusing on the education of primary and secondary schools in China, equipped with an AI adaptive learning engine based on senior algorithms and provides students with customized one-to-one education. Since its establishment, Squirrel AI Learning has raised nearly one billion yuan. Squirrel AI Learning has established a Yixue AI Lab with SRI as the primary research partner and a Joint AI Lab with Carnegie Mellon University (CMU), appointed Professor Tom Mitchell, the god-father of machine learning and the dean of computer science of CMU as the chief AI scientist, and appointed Professor Ken Koedinger of the computer science and psychology system of CMU as chief learning scientist of Squirrel AI Learning. It has made a lot of investment in the research and development of AI education. The achievements made in the past five years have made Derek Li confident enough to share his research achievements in the field of AI adaptive education before tens of thousands of participants at the conference. He first introduced the three-level architecture of Squirrel AI Learning engine. The first level mainly comprises a learning map (a persona of the students) and a map of contents (including video teaching and answering questions). The second level comprises a target management engine, a user state platform and a recommendation engine, which can develop different learning strategies for different students. The third level is an interaction system which can interact with human users. The architecture of Squirrel AI Learning system Derek Li said he often got 95 points in his childhood. His hometown lies in inland China with a population of more than 100 million. The academic competition was fierce. In order to get one more point, he had to answer 10,000 more questions. "If there was Squirrel AI Learning at that time, I would have only needed to pay attention to the 5% of questions which I did not understand." Compared with other world-leading adaptive education companies such as Dreambox, Knewton, a competitive advantage of Squirrel AI is its nano-level breakdown of knowledge points. For example, there are 300 knowledge pints in the math of junior high school. While other companies may break them down into 3,000 knowledge points, Squirrel AI can break them down into 30,000 ones. Why is the breakdown so detailed? He gave an example, "If the pixels of a camera are increased by ten times, the photos will be clearer. Similarly, if the breakdown is detailed, we can target and diagnose the students' mastery of knowledge points more accurately and save time for them." Other than imparting knowledge, Squirrel AI Learning has developed a system to enhance the students' learning ability. Derek Li called it MCM, which stands for model of thinking, capacity and method. The system can quickly detect and enhance the students' learning capacity and quality.

Last Week in AI

Every week, my team at Invector Labs publishes a newsletter to track the most recent developments in AI research and technology. You can find this week’s issue below. You can sign up for it below. Please do so, our guys worked really hard on this:From the Editor: Where We See Shapes AI Sees TexturesImage analysis is one of the hottest areas of artificial intelligence research. In recent years, AI image classification algorithms have become famous both for its progress as well as its mistakes. We all have seen the news of AI models misclassifying images of dark-skinned humans or tricky objects. While vision systems powered by AI have been able to outperform humans on some image recognition tasks under fixed conditions, they also fail miserably with the introductions of the simplest distortions. Now a team of German AI researchers has an idea why.In a recent study presented at the International Conference on Learning Representations in May, a team of researchers from the University of Tübingen in Germany highlighted the sharp contrast between how humans and machines “think,”. While humans are clearly more biased towards shapes when analyzing images, the current generation of AI techniques focuses more on textures which also introduces a lot of confusions. If this result is proven to be correct, it can help to improve the accuracy of image analysis systems to surpass humans under all sorts of conditions.Now let’s take a look at the core developments in AI research and technology this week:AI ResearchAI researchers from education powerhouse Udacity, published a paper proposing a method to generate videos lessons based on audio narrations.>Read more in this coverage from VentureBeatIn a shocking discovery, AI researchers from the University of Tübingen, Germany Proved that image recognition systems are typically bias towards textures, not shapes, which is the root caused of many misclassifications.>Read more in this coverage from Quantas MagazineIn a somewhat surprising paper, AI researcher from the University of Tartu, Estonia proposed a test that shows that deep reinforcement learning agents can learn from the perspectives of other agents.>See the complete research paper hereCool AI Tech ReleasesFacebook open sourced an implementation of a deep learning recommendation model that combines concepts of collaborative filtering and predictive analytics.>Read more in this blog post from the Facebook AI Research teamAI researchers from the Massachusetts Institute of Technology open sourced Gen, a new probabilistic programming language for causal inference.>Read more in this coverage from MIT NewsTensorFlow Lite has been ported to the Arduino IOT operating system which means that soon we will see deep learning models running on Arduino micro-controllers.>Read more in this blog post from Hackster.ioAI in the Real WorldSelf-driving startup Tier IV raised $100 million for an open source platform for autonomous vehicles.>Read more in this coverage from Crunchbase NewsIBM and MIT collaborated on launching GANPaint, a system that uses AI to help designers add, modify or remove objects to images without loosing the core context.>Read more in this coverage from MIT NewsResearchers from Harvard University use AI to try and figure out whether the authorship of some of the most disputed works in The Beatles’ back catalog can be attributed to John Lennon or Paul McCartney.>Read more in this coverage from the Financial TimesLast Week in AI was originally published in on Medium, where people are continuing the conversation by highlighting and responding to this story.

Volkswagen Joins Ford by Investing Self-Driving Startup Argo AI, Now Worth $7 Billion

Volkswagen AG and Ford Motor Co. will cooperate on electric and self-driving car technology, sharing costs on a global scale to take a major step forward in the industry’s disruptive transformation. VW will invest $2.6 billion in Ford’s autonomous-car partner Argo AI in a deal that values the operation at more than $7 billion, the two manufacturers said Friday in a joint statement in New York, confirming a figure first reported by Bloomberg. This includes $1 billion in funding and VW contributing its Audi $1.6 billion Autonomous Intelligent Driving unit. “While Ford and Volkswagen remain independent and fiercely competitive in the marketplace, teaming up and working with Argo AI on this important technology allows us to deliver unmatched capability, scale and geographic reach,” Ford Chief Executive Officer Jim Hackett said. Unprecedented shifts facing the auto industry are forcing players to consider new partnerships and potential consolidation. VW, the world’s top automaker, offers the industry’s most ambitious roll-out of electric models, while Ford, also in the top 10, is developing advanced self-driving technology with Argo. For VW, the Argo investment offers an opportunity to potentially catch up with Alphabet Inc.’s Waymo, and General Motors Co.’s Cruise unit. Road tests and accumulating huge amounts of data are critical for the further development of self-driving cars, and few apart from Waymo are equipped to do it alone. “It took a while to get this deal done, but it’s because we actually sorted out a lot of the hard problems,” Bryan Salesky, Argo AI’s co-founder and CEO, said in an interview. “We have a clear line of sight to production, vehicle supply and we have clear line of sight to where we want to go to market and how.” Besides sharing costs for the development of self-driving cars, Ford will use VW’s electric-car underpinnings that form the backbone of the most aggressive rollout of electric cars in the industry, with Volkswagen spending some 30 billion euros ($34 billion). Adding more vehicles to production lines would help gain scale and save costs, and offer Ford a platform to better comply with tougher rules on carbon-dioxide emissions in Europe. Ford will build at least one mass-market battery car in Europe starting in 2023 and deliver more than 600,000 European vehicles based on VW’s platform, dubbed MEB, over six years. A second electric model for Europe is under discussion. Teaming up with its U.S. peer is one of the key initiatives of VW Chief Executive Officer Herbert Diess to overhaul the German industrial giant. Both sides reiterated on Friday the tie-up does not include entering equity ties between Ford and VW. For Ford, a deal with VW fits with CEO Jim Hackett’s $11 billion overhaul of the company, which includes exiting the slow-selling sedan market in the U.S., shifting to focus on commercial vehicles in Europe and investing in electric-truck startup Rivian Automotive Inc. Geographically, the companies complement each other, with Ford strong in the U.S. and VW a leader in Europe and China. “Our global alliance is beginning to demonstrate even greater promise, and we are continuing to look at other areas on which we might collaborate,” VW CEO Diess said.

Will AI Take Over The World?

PART ONEThe answer you’ve been searching for, in a nutshell.Source: UnspalshThis article will take you through a journey inside the mind of a person who has no/little experience when it comes to this topic before gaining a (somewhat) comprehensive understanding of ‘Artificial Intelligence’ (AI).This journey will be broken down into 3 different parts:Part (1): How will AI intelligence supersede human intelligencePart (2): When will AI intelligence overtake the human racePart(3): What if a digital superintelligence has successfully been created and for some reason, it wants to take over the world. Will it be able to do so?FYI: This article focuses on Part (1).Before we start, I would like to clear a general misconception about the concept of AI. As nicely summarised by Tim urban:Stop thinking of robots. A robot is a container for AI, sometimes mimicking the human form, sometimes not — but the AI itself is the computer inside the robot. AI is the brain, and the robot is its body — if it even has a body. For example, the software and data behind Siri are AI, the woman’s voice we hear is a personification of that AI, and there’s no robot involved at all.How did it all start?The beginningThe term “artificial intelligence” was coined in 1956 by John McCarthy, a researcher who later founded AI labs at MIT and Stanford.Meet THE man- John McCarthyIn the early 1950s, the study of “thinking machines” had various names like cybernetics, automata theory, and information processing. McCarthy wanted a new, neutral umbrella term that could collect and organize these disparate research efforts into a single field focused on developing machines that could simulate every aspect of intelligence.During the early days, the pioneers of AI did not believe that machines could behave intelligently and definitely did not consider the possibility that machines will eventually far surpass all the intellectual activities of any man!However, when the results proved to be astonishing- computers being able to solve numerical problems, invent mathematical proofs that were more elegant than the original, follow instructions and answer questions in English, organizations like DARPA poured tens of millions of dollars into AI projects at MIT, Carnegie Mellon, Stanford!Evolution of AIOver the years, as the cost of computing started declining and processing power got more powerful, AI is now able to run a more complex algorithm on more data than ever!What are the implications?AI can now outperform human intelligence in many domains! You may be familiar with some of these ‘AI beats human champions’ events, which marked a significant advancement that happened far sooner than experts expected!Source from Valuewalk& the hype goes on. I.e. In 2019, AI triumphs against the world’s top pro team in strategy game Dota 2…..What hasn’t AI taken over the human race yet?In this article, I will attempt to break down this million-dollar question by unveiling the different factors mentioned in Bostrom’s Superintelligence book:AI’s technical limitationsDifferent paths that lead to Superintelligence (Part 1)This will allow you to form your final opinions/conclusion about the future of AI.AI’s technical challengesWhile AI has managed to beat human champions in multiple domains (Chess, dota), one might have thought that AI has mastered a high level of general intelligence to learn abstract concepts, think cleverly about strategies, compose flexible plans and make a wide range of logical deductions.You are wrong.While AI succeeded in doing essentially everything that requires thinking, AI still lacks what most 10-year-olds possess: Ordinary Common Sense.AI can mimic human tasks if they’re specific enough. They can locate and identify objects, climb, provide disaster relief, and beat humans in specific games.However, AI can only perform well in a specific well-defined task and that is why IBM’s Deep Blue built around a chess-specific algorithm won’t be able to defeat the ‘GO’ champions because it is not programmed to do so!HEY AI, IS THE MILK CARTON FULL? IF I PUT MY SWEETS IN A JAR, WILL IT STILL BE THERE TOMORROW?So unless someone were to succeed in creating an AI that could understand common sense as well as a human being, they would have succeeded in creating an AI that could do everything that human intelligence can do.When will human-level machine learning be attained?No one has a clue!Well, Bostrom did cite a survey conducted with a bunch of AI researches, and these were their opinions:10% chance of happening in 203050% chance of happening in 205090% chance of happening in 2100It could take another decade or next hundred years, no one knows! Regardless, now you know your knowledge of AI progression is not outdated.2.0. Different paths that lead to Superintelligence2.1. Artificial IntelligenceTraditional computers relied on human beings to tell them what to do and how to react. ‘Artificial Intelligence’ means equipping machines with the power to make its own decision like human beings.How to give them power?Just like how human beings store the information in their brain and learn from their patterns, scientists have also been able to use the stored machine information to make machines learn from them. This method of training computers is famously known as ‘Machine Learning’.When our human brain sees this animal, we can immediately absorb these complex information and label it as a ‘Seal’. How about if we wanted a computer to do the same task, to classify (label) photos as seal/not-seal?It’s not that simple (As explained in the earlier part of my article).Essentially, machine learning trains the AI to function as an “Object Labeler “ by showing the computer a bunch of cute seals pictures so that it can figure out what is a seal/not a seal.If humans took decades of learning to train our human brain, what about machines?Currently, AI is not able to adapt to new situations without human assistance. I.e. An AI trained in chess is not able to win a dota game.Food for thought: SEED AIIn 1950, Alan Turning came up with the notion of “child machine” where instead of trying to produce a program to stimulate the adult mind, why not train an AI to stimulate a child’s mind?Humans have been evolving and learning from their past experiences, can AI grow up too?This brings us to the concept of ‘Seed AI.’ To put it simply, Seed AI would have to possess ‘recursive self-improvement’ capabilities where it would be able to iteratively improves itself by recursively rewriting its own source code without human intervention.A Seed AI could start off with a relatively low level of intelligence. However, if it is intelligent enough to rewrite its source code to become more intelligent (that is, to become better at achieving its goal), as a result, it could become even better at rewriting its own source code to become even more intelligent. This could lead to an intelligence explosion, where the AI rapidly becomes superintelligent!Current progressAs far as we know, nobody has built a Seed AI yet. I’m not going to dive into the full discussion of the debates, but this concept is already interesting to think about.Instead of training the AI from scratch….What if we downloaded our human brain into a thumb drive and plug it into our computer?This brings us to the next possible path to superintelligence…2.2. Whole brain emulationWhole brain emulation involves scanning and closely modeling the computational structure of a biological brain to produce intelligent software.Imagine uploading Einstein/ Elon Musk/Steve jobs minds into your own body :OThis method totally reminds me of the show ‘Altered Carbon’ where the body no longer matters. As one character quipped: “You shed it like a snake sheds its skin.” That’s because the human consciousness has been digitized, and can be moved between bodies — both real and synthetic.Just showing this diagram to illustrate the complexity of this method. Guess my order ain’t coming anytime soon.Capabilities required for brain emulation (Don’t bore yourself with the details!)How far are we currently from achieving a whole human brain emulation?Well, no brains have been emulated yet. However, when the Bostrom wrote his Superintelligence book in 2014, the emulation path was believed to take another 10–15 years to gain some traction as there are several challenging technologies (Algorithm & Supercomputers) that have yet to be developed!Algorithm? Scientist cracked it!In 2018, an amazing breakthrough happened as Scientists successfully created an algorithm capable of performing a complete human brain simulation by simulating the brain’s one billion connections between individual neurons and synapses!What’s the problem then?Currently, even the most powerful supercomputers today such as the “K computer” at the Advanced Institute for Computational Science in Kobe, Japan can only tackle at most 10% of human brain simulation.Will this method be truly feasible in the future?Maybe? When future exascale supercomputers hit the scene — projected to be 10 to 100 times more powerful than today’s top performing computers — the algorithm can immediately run on those computing beasts and researchers hope to reach 100 percent simulation.Nevertheless, compared with the AI path to machine intelligence, brain emulation seems to be more feasible as it relies more on concrete observable technologies (with traction) and not wholly based on theoretical insights.2.3. Biological CognitionIf I can’t train machines to make decisions like humans or download a human brain, why not pop a pill to enhance my mental abilities?Does this concept reminds you of the show “limitless”???Unfortunately, the use of drugs to improve memory and concentration already exist in the market and getting that special ‘pill’ to spark a dramatic rise of intelligence are generally dubious and shady (Be wary of the marketing gimmicks you see on eBay/Amazon).This path extends beyond taking drugs to include ‘Manipulation of genetics’ as a way to achieve substantial improvement in cognition.The creation of the perfect babies known as ‘designer babies’Far-fetched? I think not.Dum dum dum………..Late last year (Nov 2018), a team of scientists led by Southern University of Science and Technology, Shenzhen researcher, He Jiankui, claim to use a gene-editing tool known as “CRISPR” to tailor the genes of twin girls to make them resistant to HIV.This incident is one most significant experiment in the history of human genetics and led to a renewed debate about whether designer babies are going to become a reality in the very near future.Ah, but it’s not that simple…WAIT BUT WHY?(1) Genetics research has yet to progress to the point where scientists can pinpoint the genes related to intelligence.(2) Maturational lag- What happens when the selected embryos grow into an adult human being? What if genetically modifying a specific gene (intelligence) leads to the creation of new rare and nasty diseases?(3)Genetically modifying 1 child will affect its successive generation. We are talking about an impact that will spread over multiple generations!(4)Social implications- Imagine a nation filled with smart babies only because the nation can afford the financial and technological resources. What are the implications?On an individual level: How are non-genetically modified humans going to deal with a population of genetically enhanced humans?On a global scale: Other nations will lose out in economic, scientific, military, etc. Imagine how this phenomenon will affect global equality?& the list of constraints goes on…..Will this method be truly feasible in the future?As you can see, the biological path is clearly technologically feasible and cognitive enhancement could accelerate science and technology, including progress towards Machine Learning and Brain Emulation. Imagine a world where Average Joe had the brains of Einstein/ Alan Turning.BUT (there is always a but), there are significant consequences to adopting this approach as mentioned earlier.In Summary3 potential paths to achieving Superintelligence:Machine learning- Inputting codes into a machine for it to learn. Develop SEED AI: A machine that will iteratively improve itself w/o human intervention until it becomes smarter than humansBrain Emulation- Inserting contents of a human brain into a thumb drive and plugging it into a supercomputerBiological Cognition- Undertaking less-computer-centric approaches: infant nutrition, better education, and even selective breeding2. Weak forms of superintelligence can be achieved by means of biotechnological enhancements (Education/infant nutrition/cognitive related drugs to improve memory/concentration).3. If all the ethical/scientific concerns of creating ‘perfect babies’ are resolved, this adds to the plausibility that advanced forms of machine intelligence will become more feasible.Hope Part (1) answered the “How” question and provided you with good insights on the various ways AI can potentially reach Superintelligence.Stay tuned for Part (2) on “When” (in terms of the timescale) AI can potentially supersede human intelligence! AI Take Over The World? was originally published in on Medium, where people are continuing the conversation by highlighting and responding to this story.

Deep Aging: Scientists Say AI Is Getting Better at Predicting Your True Age

Researchers say AI is getting better at guessing a person’s most important age — their biological age.You’ve heard that saying, “age is just a number.”It turns out that there’s a lot of truth to it. And, as the saying suggests, that chronological number many of us try to avoid mentioning on our birthdays is really just a number — and far less relevant than we assume. The real age we should focus on is our biological age, which is influenced by factors as diverse as genes, lifestyle, behavior and environment. Scientists say that this age — not the number that gets you into bars at 21, or lets you apply for Social Security at 62 — more closely predicts our lifespan and healthspan and is, therefore, a more accurate reflection of our true age.But, so far, scientists have found determining a person’s biological age to be extremely difficult, if not impossible.Now, a team of scientists report that the accurate prediction of biological age is becoming more feasible, thanks to artificial intelligence and new, vast publicly available datasets. The researchers summarized the current work on deep aging clocks in a recent issue of Trends in Pharmacological Sciences.According to Alex Zhavoronkov, Ph.D, founder and CEO of Insilico Medicine, people are good at using images, videos, voice and even smell to guess another person’s age. But, AI can guess ages even more accurately, he said in a news release.“Deep neural networks can do it better and we can now interpret what factors are most important,” said Zhavoronokov. “Very often when someone looks older than their chronological age, they are sick. A trained doctor can guess the health status of a patient just by looking at him or her. At Insilico we developed a broad range of deep biomarkers of aging that can be used by the pharmaceutical and insurance companies, as well as by the longevity biotechnology community. In this paper we describe the recent progress in this emerging field and outline a range of non-obvious applications.”The researchers suggest that there’s not just one indicator — or biomarker — that can tip off a person’s biological age, but a combination of these different predictors. AI is suited for this task because it is adept at sorting through all the available data and determining the right combination of factors.Accurately estimating biological ages won’t just be a cool trick for AI systems to perform at birthday parties, either. The researchers said that identifying the predictors of aging could help scientists better understand the aging process and reveal keys to healthy aging. Pharmaceutical and insurance companies, as well as medical and longevity industries, would all be keenly interested in that trend.“Deep biomarkers of aging developed utilizing a variety of data types of aging are rapidly advancing the longevity biotechnology industry. Using biomarkers of aging to improve human health, prevent age-associated diseases and extend healthy life span is now facilitated by the fast-growing capacity of data acquisition, and recent advances in AI. They hold a great potential for changing not only aging research, but healthcare in general,” said Polina Mamoshina, senior scientist at Insilico Medicine, in the news release.The researchers predict that AI and aging research will accelerate in the future and note that acceleration is being driven by the increasing number of people entering this field — from universities to corporations — as well as the number of funding sources adding money to those organizations. Aging: Scientists Say AI Is Getting Better at Predicting Your True Age was originally published in on Medium, where people are continuing the conversation by highlighting and responding to this story.
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Have you heard about pilotless vehicles on blockchain?

As a byproduct of this development, the partnership between Boeing and SparkCognition 'will also provide a standardized programming interface to support package delivery, industrial inspection and other commercial applications', according to Boeing press release


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OKEx’s Token Will See 17x Growth in Price, Blockchain Investment Firm Forecasts

The recent popularity Initial Exchange Offerings (IEO) have been experiencing over the last few months has brought what it appears to be like a new bull run. An incredible example of success is the OKB token issued by OKEx, one of the largest cryptocurrency exchanges by trading volume in the world, which has impressed the market beyond expectations having experienced an increase in the price of 163% since launched. Shinobi Capital, a leading blockchain advisory firm and also an investor in early-stage start-ups, has released its latest evaluation report estimating that the OKB price will grow further to US$30.75, 17 times the current price, by the end of 2020. According to Jason Hill, the founding partner of Shinobi Capital:  Exchanges tokens will be the powerhouse of the development of the digital asset market and even blockchain technology. Following a series of IEOs powered by exchange tokens in H1 2019, the market is marching to the next round of bull run. OKB, as a market leader of exchange tokens, is also welcoming its own uptrend. Backed by a number of use cases and a large user base of OKEx, OKB has demonstrated a huge potential of growth. The established blockchain and cryptocurrency advisory firm is well-known for its extensive experience in start-up investment. In their evaluation report of OKB, Shinobi Capital lays out the two most important factors that will affect OKB’s future trading volume, the development of the OKChain mainnet and the overall the crypto market condition. Furthermore, the report also establishes a comparative evaluation model with other major platform tokens, including Binance Coin (BNB), EOS, and TRON (TRX). This comparative evaluation studies different aspects of each token such as trading volume, price patterns, and usage demand. The latest OKB buy-back & burn program is also taken into account to evaluate the token’s future price trend. It is expected that by the end of 2020, the price of OKB will reach USD30.75 and its market capitalization will be about USD7.068 billion. There is a significant growth lag in OKB at this stage, and the potential of price growth needs to be further released. In the next round of market recovery and boom, OKB is likely to become one of the fastest-growing assets in the market. Disclosure: This is a sponsored press release The post OKEx’s Token Will See 17x Growth in Price, Blockchain Investment Firm Forecasts appeared first on NullTX.

Ethereum Classic presents roadmap to improve DApp development and overall infrastructure

Experts of the cryptospace are making use of DApp services to simplify the entire blockchain process. Along these lines, Ethereum Classic’s [ETC] core development team has put forth an initiative to revamp its existing ecosystem. Moving forward with an aspiration for refinement, ETC’s team is developing fresh features over blockchain technology, a development that will […] The post Ethereum Classic presents roadmap to improve DApp development and overall infrastructure appeared first on AMBCrypto.

David Marcus Grilled During Facebook's Senate Hearing

During the first of two congressional hearings regarding Facebook's Libra cryptocurrency, project lead David Marcus went as far as to say he'd be willing to take his salary in Libra after intense grilling from Senator Sherrod Brown.
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David Marcus Questioned Over Libra by Congress

Facebook is finally facing its reckoning as David Marcus, head of the company’s blockchain division and the leader of Libra, the venture’s new cryptocurrency, was grilled by Senate members who refuse to believe in Facebook’s allegedly noble financial plans. Libra: A Congressional Issue? Many people have a hard time believing in Facebook’s morality following the Cambridge Analytica scandal. Discovered in 2018, Facebook had allegedly been selling users’ private data for years to third parties for advertising purposes. Following Mark Zuckerberg’s Senate hearing last year, trust in Facebook has fallen to an all-time low. In many ways, this new “congressional step” for the social media conglomerate should serve as a huge learning experience. When you’re a company of Facebook’s size and you do anything to compromise the safety or privacy of your customers, you can bet it’s going to take a long time to earn their trust back. Facebook is learning this lesson in spades right now, as several Senators taking part in the hearing commented about the lack of trust they feel towards the company and its executive team. Sherrod Brown, a Democrat from the state of Ohio, commented:  Facebook has demonstrated through scandal after scandal that it doesn’t deserve our trust. We’d be crazy to give them a chance to let them experiment with people’s bank accounts. Senator Martha McSally, a Republican from Arizona, stated that Facebook is simply trying to shift gears and get people to focus on something else entirely, in this case cryptocurrency. Rather than seriously attempt to fix its reputation, it’s working to divert people’s attention with an entirely new product. She says: I don’t trust you guys. Instead of cleaning up your house, you are launching into a new business model. In addition, Facebook is also being criticized for its complete lack of coordination with policymakers. Throughout the early development of Libra, Facebook’s executive team failed to make any contacts with regulators or legal authorities to potentially understand how the cryptocurrency could better satisfy present financial laws and terms.  Trust Takes a Long Time to Build At least David Marcus isn’t lying to himself. He acknowledged during the hearing that it would likely take a while before the company can earn people’s trust well enough that they would provide their banking details. He states:  I want to make it clear that we are only at the beginning of the journey. We expect the review of Libra to be one of the most extensive ever. Facebook will not offer the Libra currency until we have addressed the concerns and receive appropriate approvals… We will not control Libra and will be one of over 100 participants that will govern over the currency. We will have to gain people’s trust if we want people to use our network over the hundreds of competing companies. The post David Marcus Questioned Over Libra by Congress appeared first on Live Bitcoin News.
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Waves CEO Has Sold His Stake At Vostok, A Waves Blockchain Affiliated Project

Alexander Ivanov, the founder, and CEO of Waves has recently decided to fully sell his stake of Vostok, blockchain spin-off of the Waves platform. According to a recent press release, now only the GHP Group, which bought all the stakes, will be the owner of the project. The CEO also affirmed on the press release that […]
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