Coinbase says it halted more than $280,000 in bitcoin transactions during Twitter hack – The Verge

The cryptocurrency exchange Coinbase said that it stopped around 1,100 customers from sending bitcoin to hackers who gained access to high-profile Twitter accounts last week.

Last Wednesday, over 100 Twitter accounts, some belonging to major companies like Apple and high-profile people like Vice President Joe Biden and Bill Gates, were hacked as part of a massive coordinated bitcoin scam. According to Twitter, the hackers were able to convince some of the companys employees to use internal systems and tools to access the accounts and help the hackers defraud users into sending them bitcoin.

According to Forbes, Coinbase and other cryptocurrency exchanges were able to stop some customers from sending bitcoin to the hackers by blacklisting the hackers wallet address. Specifically, Coinbase says it prevented just over 1,000 customers from sending around $280,000 worth of bitcoin during last Wednesdays attack. Roughly 14 Coinbase users sent around $3,000 worth of bitcoin to the scams bitcoin address before the company moved to blacklist it, the company said.

We noticed the scam and began blocking transactions within a couple of minutes of the initial wave of scam posts, a Coinbase spokesperson told The Verge on Monday.

Twitter accounts belonging to cryptocurrency exchanges including Binance and Gemini were also targeted during Wednesdays attack. Coinbases chief information officer told Forbes on Sunday that it learned of the scam shortly after tweets were posted from fellow exchanges accounts.

As of Monday, Twitter is still investigating Wednesdays attack. On Friday, the company put out a blog post confirming that 130 accounts were targeted and the hackers were able to initiative a password reset, log in to the account, and send tweets for 45 of those accounts. Twitter also said that the hackers were able to download account data belonging to eight unverified users.

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Coinbase says it halted more than $280,000 in bitcoin transactions during Twitter hack - The Verge

Bitcoin Interest Wanes As A Violent Breakout Looms – Forbes

Volatility Ahead Caution Sign - Blue Sky Background

Since the halving, bitcoins price has traded mostly between $9,000 and $10,000, compared to the red hot DeFi and alt coins that regularly post triple digit returns. In recent weeks, bitcoins range has tightened with many analysts noting a potentially violent price breakout on the horizon. However, there is no consensus on which direction the breakout will assume.

Charles Edwards, Founder of Capriole Investments, suggests bitcoins fundamentals have never looked better. I have a very bullish outlook in the mid to long term. For example, energy value is at all time highs, suggesting BTC is more valuable than ever before. When this is increasing, it is historically very bullish. This longer term indicator may suggest that a breakout leans towards the bulls.

Interestingly, bitcoins tightening volatility is not a new phenomenon, and occurred from late-September to early-November 2018, which ultimately broke out to the downside, falling from $6,500 to $3,400. One quantitative risk indicator value has been dropping quickly coupled with compressing price volatility. The only other time this dynamic unfolded was November 2018, which could suggest that a stark price fall is on the horizon. The caveat is that this signal has only occurred once before, thus suffers from a small sample size.

https://weeklyjab.substack.com/p/weekly-jab-bitcoin-analysis-3

Additionally, the anonymous Founder of Decentrader, Filb, notes derivatives open interest (OI) increasing as we have consolidated through this period by about 45%, is a similar amount seen before the fall in Q3 last year, to around 8k. It appears OI has been net increasing on dumps. This implies that...the market needs a catalyst to clear this OI out.

Tradingview.com, Decentrader.com

Furthermore, Filb adds, alts have continued their downward trajectory over the past few days, which are probably quite important as to what happens next; particularly if they start dumping and the money flowing back into bitcoin isnt doing anything. Something to pay attention to for sure.

Lastly, Bo Collins, CEO of San Juan Mercantile Bank and Trust, notes bitcoin CME futures volume growth from 2019 to 2020 is only approximately +10%, at the time of writing. This number becomes weaker when considering yearly foreign exchange (FX) futures volume growth can regularly eclipse +30%, e.g. 2018. Tepid bitcoin futures growth calls into question the institutional adoption narrative in some respects, and may imply less buying demand than originally suspected.

However, as shown by Glassnode.io, the amount of bitcoin held on centralized exchanges has dropped considerably since March, which seems bullish for bitcoin as spot investors appear to be holding for the long-term rather than short-term trading.

https://glassnode.com/

Furthermore, per Blockchair.com, bitcoin days destroyed supports the aforementioned notion, with 2020 metrics well within the historical average, including far smaller spikes than previous all-time highs, thus bullish.

https://blockchair.com/bitcoin/charts/coindays-destroyed?interval=full

Despite the differing analyses, the only thing that is certain, is that a strong breakout for bitcoin looms. Only time will tell which faction of analysts are proven correct.

Disclosure: The author owns bitcoin and ethereum.

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Bitcoin Interest Wanes As A Violent Breakout Looms - Forbes

Bitcoin And Other Crypto-Assets Excluded From Central Bank Experiments – Forbes

A picture taken on January 15, 2020 shows the facade of the Banque de France building in Paris. The ... [+] bank is working with the European Central Bank to re-imagine how new technologies can change the way money works.

The central bank of France is on the verge of conducting a series of sweeping experiments whose lessons could be used to change the way money works. Cryptocurrency wont be included. In a statement from the Banque de France, the nations central bank, which works together with the European Central Bank to determine the monetary policy of the continent, the institution today released the names of eight participants in the experiments and the scope of the work.

Participants are consulting giant Accenture ACN , settlement giant Euroclear, the HSBC bank, French firm, Iznes, etheruem platform LiquidShare, little-known startup, ProsperUS, crypto bank Seba, and Forge, Societe Generales digital capital markets spinoff. The broad parameters of the experiments include everything from testing regulation using digital currency to improve cross-border payments, an analysis of how a central bank digital currency should be made available, and importantly, to explore new methods of exchanging financial instruments (excluding crypto-assets) for central bank money.

The statement from one of the words leading central banks shows how the vaunted institutions are scrambling to learn the best that cryptocurrency, and its underlying blockchain technology have to offer, but only within limits. Neither blockchainthe shared ledger that lets bitcoin existnor the more sanitized word to describe the larger group of technologiesdistributed ledger technology were mentioned by name in the statement. As such, the work also helps define the limits of what any actual adoption of the technology might look like.

The strong mobilization around this call for candidates testifies to the interest of the actors of finance and technology for this approach aiming to explore the potential contributions of a digital money issued by the central bank to improve the functioning of financial markets, in particular interbank regulations, according to a Google GOOGL translation of the statement. A representative of the Banque de France declined to share any additional context.

Over the coming days, the Banque de France will begin conducting experiments with each of the candidates, according to the statement, with some of the projects expected to take as long as multiple months. Candidates were asked to respond to the banks call for applications for CBDC experiments by May 15. The experiments could have far-reaching implications to the decision-making processes for the central bank, which in addition to helping define Europes monetary policy and implement it in France, regulates Frances banks and insurance companies and ensures risk management.

Beyond the confines of France though, lessons learned from the central bank digital currency experiments will be contributed to the international work being led by the Eurosystem, the monetary authority of the European Union. Earlier this month, the bank joined Germanys central bank, the Deutsche Bundesbank, and the European Central Bank in co-hosting a new innovation center in Europe within the framework of the Innovation Hub of the Bank for International Settlements.

In May, European Central Bank executive board member, Yves Mersch, confirmed in a speech at industry conference Consensus, that the European Central Bank was one of at least 66 central banks exploring how lessons learned from blockchain could change the very fabric of what we consider money.

For example, Chinas central bank, the Peoples Bank of China, has taken a giant first-mover advantage in the space, starting its CBDC experiments years ago, and currently testing a working implementation. If successful, one side-effect of CBDCs could be borderless transactions, possibly giving people the choice to store Chinese Renminbi in addition to, or instead of dollars, as a global reserve currency,

Based on what we know of the nearly pervasive experiments around the world looking into the nature of CBDCs, some of the other possible changes to the way money works could include giving citizens accounts at central banks, allowing them to occasionally bypass commercial banks and receive direct access to stimulus checks and more. Another possible, but controversial side-effect of central bank digital currencies could enable online payments while maintaining the privacy citizens have historically enjoyed with cash.

Skeptics of the CBDC concept argue that so long as central banks continue to have the authority to print or issue nearly unlimited amounts of the currency the underlying problems of inflation will continue to drive people to more distributed, deflationary alternatives such as bitcoin, which has a set amount. Other skeptics point to the unlikelihood that central banks will ever actually allow citizens the same privacy they have in the real world, online, and could use the technology as a way to track their own citizens spending habits.

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Bitcoin And Other Crypto-Assets Excluded From Central Bank Experiments - Forbes

Bitcoin SV DevCon 2020: Craig Wright wants to make the world better with Bitcoin – CoinGeek

Since he was young, Dr. Craig Wright has been fascinated with coding. In his fireside chat at the Bitcoin SV DevCon 2020 virtual event, Dr. Wright talked about his passion for coding, why he has a very strong drive to make the world better, and the future of Bitcoin.

Dr. Wrights fireside chats are among the events that everyone in the digital currency and blockchain industries awaits eagerly. The latest one didnt disappoint, with Bitcoins creator delving into the technical side of Bitcoin while also sharing more about what drives him.

Dr. Wright started coding when he was a kid, creating games to fight boredom. His love for creating has led him to grow his skills over the years, ultimately culminating in the creation of Bitcoin. Discipline has been one of his core values, he told nChain CTO Steve Shadders.

Dr. Wright is also quite passionate about educating people, saying,

If we want to get past this post-modern, deconstructionist, anti-life and nihilistic view of the world, then we have to have people who are involved and educated. I want to live in a world thats better. That means people who are engaged in society, who are active and understand the issues affecting them.

Dr. Wright also delved into the technical aspects of Bitcoin. On whether we should bring back substring opcodes, Satoshi believes that we may be past that as Bitcoin goes into application stage. Such changes would now affect third parties who are building on the Bitcoin blockchain.

Dr. Wright further revealed why he chose to use Forth language in developing Bitcoin.

Forth is very small and efficient. Its a language Ive used a lot in my past. Its very easy to write good code that you can find the errors and validate the results very quickly. [] If you screw up on Forth, it might work. If you screw up on Java, it might work, but every now and again, you get strange results.

The Bitcoin creator has gone through trying times, both from within the Bitcoin world and beyond, but he says he is still proud about many things that his invention has introduced the world to. One of these is the accelerated pace of adoption in recent years. With the number of applications building on top of Bitcoin rising by the day, its only a matter of time before the world is running on Bitcoin, he concluded.

The thing that makes me happy about Bitcoin is that people are finally starting to get it.

New to Bitcoin? Check out CoinGeeksBitcoin for Beginnerssection, the ultimate resource guide to learn more about Bitcoinas originally envisioned by Satoshi Nakamotoand blockchain.

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OpenStack Community Delivers Future of Bare Metal: White Paper Details Maturity and Adoption of Ironic Bare Metal as a Service – Benzinga

Latest collaboration showcases how Ironic open source software delivers abstraction and automation for container workloads including production case studies.

AUSTIN, Texas (PRWEB) July 20, 2020

Today, the Ironic community published a white paper that highlights the scope, growth and maturity of the bare metal provisioning software. The white paper was developed by more than 26 contributors over 12 months and details all aspects of bare metal provisioning and lifecycle management via the OpenStack project. It provides information on performance, security, compliance and stack independence, as well as non-virtualizable resources associated with bare metal.

The white paper is a deep dive into the tools, clients and automation that demonstrate how the mature Ironic software delivers stable, production-proven bare metal compute instances, a popular option for deployment of container-based workloads because bare metal avoids the overhead and performance penalties common with full-featured hypervisors such as KVM.

*** Download now: "Building the Future on Bare Metal: How Ironic Delivers Abstraction and Automation using Open Source Infrastructure" here: https://www.openstack.org/bare-metal/how-ironic-delivers-abstraction-and-automation-using-open-source-infrastructure

Ironic Case Studies Highlighted

The white paper includes case studies from users including StackHPC, SuperCloud, Red Hat, VEXXHOST and more. Use cases highlighted in these stories include:

Julia Kreger, Ironic Project Team Lead, recalled an anecdote about hearing first-hand about the value of the Ironic software: "At a conference a few years ago, I sat down to dinner next to someone I did not know. He started to tell me of his job and his long hours in the data center. He asked me what I did, and I told him I worked as a software engineer in open source. And he started talking about some tooling he recently found that took tasks that would normally take nearly two weeks for racks of servers, to just a few hours. He simply glowed with happiness because his quality of life and work happiness had exploded since finding this Bare Metal as a Service tooling called Ironic. As a contributor, this is why we contribute. To make those lives better."

The paper explores how the Open Infrastructure community has addressed the bare metal provisioning problem with entirely free open source software. It discusses the issues operators face in discovering and provisioning servers, how the OpenStack community has solved these issues with Ironic and the future of open infrastructure and hardware management, emphasizing the necessity of open source and the value of contributors continuing to build on top of strong foundations. For operators interested in deploying Ironic, they select a partner from the dozens of vendors in the Ironic Bare Metal Program.

About the OpenStack Foundation and Ironic

Ironic is an open source project that fully manages bare metal infrastructure and is part of OpenStack. The OpenStack Foundation (OSF) supports the development and adoption of open infrastructure globally, across a community of over 100,000 individuals in 187 countries, by hosting open source projects and communities of practice, including datacenter cloud, edge computing, NFV, CI/CD and container infrastructure.

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OpenStack Community Delivers Future of Bare Metal: White Paper Details Maturity and Adoption of Ironic Bare Metal as a Service - Benzinga

Leading the Intelligence Community After 2020’s Upheavals – GovExec.com

As he settles into his role as Director of National Intelligence, John Ratcliffe has the opportunity to lead the intelligence community through a series of unprecedented national crises and position it to thrive in the new normal. Given laws on civil service hiring, handling classified information, and other requirements unique to the national security apparatus, intelligence agencies may be less able than private companies to reinvent their business models on the fly. However, adversity often presents opportunity.

In the wake of 2020s transformative events, U.S. intelligence agencies must challenge longstanding institutional habits and embrace new ways to meet the nations strategic challenges.

DNI Ratcliffe should consider new approaches to four vital functions that support the ICs core missions.

First, enable more unclassified work and expand remote work polices. The public health emergency has demonstrated that the IC must change the way it works with information. Currently, intelligence personnel work in secure facilities where they process sensitive data to produce classified reports. The COVID crisis has prevented full staffing in these cramped secure spaces and highlighted the need for secure mobile communications that could enable some types of classified work at home. After years of discussing the potential of secure remote work, the Army is launching a program to allow remote access to certain types of classified data. With telework proving to be a critical tool to contain the spread of COVID-19, now is the time to turn these ideas into reality.

Due to the current health crisis, many IC analysts are working remotely, drawing on open source information like social media trend analysis and satellite imagery. Why not keep them at home, where they can produce insightful unclassified evaluations of threats like foreign disinformation, sanctions-busting, and threats to critical infrastructure? Until remote classified access becomes widespread, analysts working in secure spaces can add nuance to unclassified analyses by incorporating sensitive data. This way, the IC can produce the comprehensive all-source intelligence assessments senior policymakers expect while also generating unclassified reports that can be shared with private sector partners, allies, and the public.

Second, embrace collaboration with non-government partners to protect critical sectors of the economy from cyberattack and economic espionage. Given the importance of critical infrastructure services to Americans health and safety, the intelligence community, which typically serves senior government decision makers, needs to think of these private entities as customers as well. If the NSA collects intelligence that indicates foreign hackers plan to attack the healthcare sector, for example, it must provide warnings to sector representatives at a classification level that enables prompt action. At present, a private sector target could only be warned after a lengthy declassification process or after a Homeland Security component lacking all of the original intelligence developed a releasable assessment.

Third, enhance collaboration with the private sector to develop advanced technologies. We no longer live in a world where we have to hunt for data. Instead, drowning in data, we must use technical tools to find value in the information we already possess. The IC must be a leader in the application of game-changing technologies like artificial intelligence and quantum computing, but it must partner closely with private sector innovators to get there.

Fourth, ensure opportunities for underrepresented groups. As widespread rage over systemic racism and social injustice has shown, all organizations must take steps to ensure all voices are heard, valued, and incorporated. Agency leaders must commit to recruiting more minority candidates, ensuring minority employees have equal opportunities for career advancement, and increasing diversity in their leadership ranks. The security clearance process must develop more sophisticated ways of evaluating first- and second-generation Americans foreign ties so more immigrants and children of immigrants can be hired.

Previous transformational events, such as the end of the Cold War and the 9/11 terrorist attacks, forced agencies to adopt new missions and adapt to new threats. The men and women of the intelligence community rose to the challenge then, and they will do so again in response to the unprecedented events of 2020. As DNI Ratcliffe sets his priorities, we encourage him to seize the moment and make these needed reforms to position the intelligence community for the years ahead.

Letitia A. Long, the former director of the National Geospatial-Intelligence Agency, is chairman of the board of the Intelligence and National Security Alliance, an association promoting public-private collaboration on intelligence matters. Tish can be reached at tlong@insaonline.org.

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Leading the Intelligence Community After 2020's Upheavals - GovExec.com

AAAI 2020 Conference | Thirty-Fourth AAAI Conference on …

For general inquiries about the AAAI-20 program, please write to aaai20@aaai.org.

A selection of talks from AAAI-20 will be livestreamed and available on this page.

General Chair:

Francesca Rossi (IBM Research, USA)

Program Cochairs:

Vincent Conitzer (Duke University, USA)

Fei Sha (Google Research and University of Southern California, USA)

SPECIAL EVENT

The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20) will be held February 7-12, 2020 at the Hilton New York Midtown, New York, New York, USA. The program chairs will be Vincent Conitzer (Duke University, USA) and Fei Sha (University of Southern California, USA). The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. AAAI-20 will have a diverse technical track, student abstracts, poster sessions, invited speakers, tutorials, workshops, and exhibit and competition programs, all selected according to the highest reviewing standards. AAAI-20 welcomes submissions on mainstream AI topics as well as novel crosscutting work in related areas.

Additional details about AAAI-20 will be posted here as they become available.

Past AAAI Conferences

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AAAI 2020 Conference | Thirty-Fourth AAAI Conference on ...

Could Super Artificial Intelligence Be, in Some Sense …

Tech writer Ben Dickson poses the question:

Should you feel bad about pulling the plug on a robot or switch off an artificial intelligence algorithm? Not for the moment. But how about when our computers become as smartor smarterthan us?

Philosopher Borna Jalenjak (above right) of the Luxembourg School of Business has been thinking about that. He has a chapter, The Artificial Intelligence Singularity: What It Is and What It Is Not, in Guide to Deep Learning Basics: Logical, Historical and Philosophical Perspectives, in which he explores the case for thinking machines being alive, even if they are machines. The book as a whole presents unique perspectives on ideas in deep learning and artificial intelligence, and their historical and philosophical roots.

Dickson explains,

Singularity is a term that comes up often in discussions about general AI. And as is wont with everything that has to do with AGI, theres a lot of confusion and disagreement on what the singularity is. But a key thing that most scientists and philosophers agree that it is a turning point where our AI systems become smarter than ourselves. Another important aspect of the singularity is time and speed: AI systems will reach a point where they can self-improve in a recurring and accelerating fashion.

Said in a more succinct way, once there is an AI which is at the level of human beings and that AI can create a slightly more intelligent AI, and then that one can create an even more intelligent AI, and then the next one creates even more intelligent one and it continues like that until there is an AI which is remarkably more advanced than what humans can achieve, Jalsenjak writes.

No. Wait. Is there clear evidence that less intelligent entities can simply create more intelligent ones? Consider,

A recent paper on the evolution of learning explores how computers could begin to evolve learning in the same way as natural organisms did. The authors use Avida, a software program for simulating evolution, to support their claim.

Avida was originally intended to demonstrate how Darwinian evolution, which could occur without design in nature, is supposed to work. However, as many have shown, the program actually ended up demonstrating quite conclusively the need for design. This latest paper on using Avida to simulate the evolution of learning has shown the same thing.

Many people do sincerely believe that higher intelligence can just somehow evolve from lower intelligence. But sincere belief isnt evidence. And Dickson stresses, To be clear, the artificial intelligence technology we have today, known as narrow AI, is nowhere near achieving such feat. So we are talking about whether superintelligent AI, if it ever arrives, can be considered alive.

And that is a more complex question than we might at first suppose. First there are 123 definitions of life out there, with different sciences tending to prefer their own:

It is surprisingly difficult to pin down the difference between living and non-living things

To make matters worse, different kinds of scientist have different ideas about what is truly necessary to define something as alive. While a chemist might say life boils down to certain molecules, a physicist might want to discuss thermodynamics.

The classic borderline case is viruses. They are not cells, they have no metabolism, and they are inert as long as they do not encounter a cell, so many people (including many scientists) conclude that viruses are not living, says Patrick Forterre, a microbiologist at the Pasteur Institute in Paris, France.

For his part, Forterre thinks viruses are alive, but he acknowledges that the decision really depends on where you decide to place the cut-off point.

Arguing that for the panpsychist view that electrons may be conscious, Tam Hunt makes the point that

Many biologists and philosophers have recognized that there is no hard line between animate and inanimate. J.B.S. Haldane, the eminent British biologist, supported the view that there is no clear demarcation line between what is alive and what is not: We do not find obvious evidence of life or mind in so-called inert matter; but if the scientific point of view is correct, we shall ultimately find them, at least in rudimentary form, all through the universe.

Niels Bohr, the Danish physicist who was seminal in developing quantum theory, stated that the very definitions of life and mechanics are ultimately a matter of convenience. [T]he question of a limitation of physics in biology would lose any meaning if, instead of distinguishing between living organisms and inanimate bodies, we extended the idea of life to all natural phenomena.

So there isnt a simple rule we can apply.

That said, some of the arguments for AI as a form of life sound suspiciously like the arguments around extraterrestrial beings:

Theres great tendency in the AI community to view machines as humans, especially as they develop capabilities that show signs of intelligence. While that is clearly an overestimation of todays technology, Jasenjak also reminds us that artificial general intelligence does not necessarily have to be a replication of the human mind.

That there is no reason to think that advanced AI will have the same structure as human intelligence if it even ever happens, but since it is in human nature to present states of the world in a way that is closest to us, a certain degree of anthropomorphizing is hard to avoid, he writes in his essays footnote.

Very well, but thats what they tell us about the so-far undetected extraterrestrials: They might be a form of life we dont recognize as such. One can never disprove such a proposition but, as before, it does not amount to evidence for anything.

Then there is the question of purpose:

There are different levels to life, and as the trend shows, AI is slowly making its way toward becoming alive. According to philosophical anthropology, the first signs of life take shape when organisms develop toward a purpose, which is present in todays goal-oriented AI. The fact that the AI is not aware of its goal and mindlessly crunches numbers toward reaching it seems to be irrelevant, Jalsenjak says, because we consider plants and trees as being alive even though they too do not have that sense of awareness.

Again, wait. Sophisticated computers have exclusively the purposes that humans program into them in our own interests, as do smart ovens and self-driving cars. These objects have no intrinsic purpose.

Plants have their own intrinsic purposes, which humans did not create, of growing and producing seeds. Humans can use plants and even trick them into doing something that is not part of their intrinsic purpose (seedless grapes, for example). But the original purpose is theirs. So we can give plants, but not computers, credit for purpose in life.

Jalenjak goes on to argue that AI can be alive even though it does not need to reproduce itself because it can, after all, just replace worn-out parts. But that fact alone is evidence that an AI entity is not alive. Life forms must reproduce themselves in a vast variety of ways because they are, generally, unitary beings, not a collection of swappable parts.

And what about self-improvement, which is regarded by some as part of a definition for life?

Todays machine learning algorithms are, to a degree, capable of adapting their behavior to their environment. They tune their many parameters to the data collected from the real-world, and as the world changes, they can be retrained on new information. For instance, the coronavirus pandemic disrupted may AI systems that had been trained on our normal behavior. Among them are facial recognition algorithms that can no longer detect faces because people are wearing masks. These algorithms can now retune their parameters by training on images of mask-wearing faces. Clearly, this level of adaptation is very small when compared to the broad capabilities of humans and higher-level animals, but it would be comparable to, say, trees that adapt by growing deeper roots when they cant find water at the surface of the ground.

Tree roots? Digging deeper for water is hardly their greatest accomplishment. They are very complex systems, used by the trees for, among other things, exchanging information with other trees:

Researchers are unearthing evidence that, far from being unresponsive and uncommunicative organisms, plants engage in regular conversation. In addition to warning neighbors of herbivore attacks, they alert each other to threatening pathogens and impending droughts, and even recognize kin, continually adapting to the information they receive from plants growing around them. Moreover, plants can talk in several different ways: via airborne chemicals, soluble compounds exchanged by roots and networks of threadlike fungi, and perhaps even ultrasonic sounds. Plants, it seems, have a social life that scientists are just beginning to understand.

Plants are not thought by botanists to be conscious but they do communicate extensively without a mind or brain. Nor, and this is the main point, do they need humans to program them or teach them anything. It all happens with or without our knowledge, let alone our involvement.

Jalenjak seems undeterred. He challenges us, Are characteristics described here regarding live beings enough for something to be considered alive or are they just necessary but not sufficient?

And Dickson responds,

Having just read I Am a Strange Loop by philosopher and scientist Douglas Hofstadter, I can definitely say no. Identity, self-awareness, and consciousness are other concepts that discriminate living beings from one another. For instance, is a mindless paperclip-builder robot that is constantly improving its algorithms to turn the entire universe into paperclips alive and deserving of its own rights?

So Dickson doesnt seem convinced. Still, he offers,

But like many other scientists, Jalsenjak reminds us that the time to discuss these topics is today, not when its too late. These topics cannot be ignored because all that we know at the moment about the future seems to point out that human society faces unprecedented change, he writes.

Maybe. But then again, maybe not.

The time to discuss this is now! implies that the scenario described must happen so we have no choice but to prepare. Perhaps the discussion we should have first is, how plausible are the arguments that whatever AI apocalypse is proposed must happen? In this case, Jalenjak didnt succeed in convincing Dickson that super AI should be considered alive. Maybe we dont need to have the discussion now, except as Sci-Fi Saturday food for thought.

The whole field could probably benefit from a dose of common sense and skepticism.

You may also enjoy:

Which is smarter? Babies or AI? Not a trick question Humans learn to generalize from the known to the unknown without prior programming and do not get stuck very often in endless feedback loops.

AI expert: Artificial intelligences are NOT electronic people. AI makes mistakes no human makes, so some experts are trying to adapt human cognitive psychology to machines. David Watson of the Alan Turing Institute fills us in on some of the limitations of AI and proposes fixes based on human thinking.

and

AI will fail, like everything else, eventually The more powerful the AI, the more serious the consequences of failure Overall, we predict that AI failures and premeditated malevolent AI incidents will increase in frequency and severity proportionate to AIs capability.

Link:
Could Super Artificial Intelligence Be, in Some Sense ...

Artificial intelligence vs Machine Learning vs Deep …

Nowadays many misconceptions are there related to the words machine learning, deep learning and artificial intelligence(AI), most of the people think all these things are same whenever they hear the word AI, they directly relate that word to machine learning or vice versa, well yes, these things are related to each other but not the same. Lets see how.

Machine Learning:Before talking about machine learning lets talk about another concept that is called data mining. Data mining is a technique of examining a large pre-existing database and extracting new information from that database, its easy to understand, right, machine learning does the same, in fact, machine learning is a type of data mining technique.

Heres is a basic definition of machine learning Machine Learning is a technique of parsing data, learn from that data and then apply what they have learned to make an informed decision

Now a days many of big companies use machine learning to give there users a better experience, some of the examples are, Amazon using machine learning to give better product choice recommendations to there costumers based on their preferences, Netflix uses machine learning to give better suggestions to their users of the Tv series or movie or shows that they would like to watch.Deep Learning: Deep learning is actually a subset of machine learning. It technically is machine learning and functions in the same way but it has different capabilities.

The main difference between deep and machine learning is, machine learning models become better progressively but the model still needs some guidance. If a machine learning model returns an inaccurate prediction then the programmer needs to fix that problem explicitly but in the case of deep learning, the model does it by himself. Automatic car driving system is a good example of deep learning.

Lets take an example to understand both machine learning and deep learning Suppose we have a flashlight and we teach a machine learning model that whenever someone says dark the flashlight should be on, now the machine learning model will analyse different phrases said by people and it will search for the word dark and as the word comes the flashlight will be on but what if someone said I am not able to see anything the light is very dim, here the user wants the flashlight to be on but the sentence does not the consist the word dark so the flashlight will not be on. Thats where deep learning is different from machine learning. If it were a deep learning model it would on the flashlight, a deep learning model is able to learn from its own method of computing.Artificial intelligence: Now if we talk about AI, it is completely a different thing from Machine learning and deep learning, actually deep learning and machine learning both are the subsets of AI. There is no fixed definition for AI, you will find a different definition everywhere, but here is a definition that will give you idea of what exactly AI is.AI is a ability of computer program to function like a human brain

AI means to actually replicate a human brain, the way a human brain thinks, works and functions. The truth is we are not able to establish a proper AI till now but we are very close to establish it, one of the examples of AI is Sophia, the most advanced AI model present today. The reason we are not able to establish proper AI till now is, we dont know the many aspects of the human brain till now like why do we dream ? etc.

Why people relate machine learning and deep learning with artificial intelligence?Machine learning and deep learning is a way of achieving AI, which means by the use of machine learning and deep learning we may able to achieve AI in future but it is not AI.

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What Is Artificial Intelligence | Artificial Intelligence Wiki

What is Artificial Intelligence (AI)?

The emergence of artificial intelligence (AI) has played a key part in ushering in the Fourth Industrial Revolution. According to the World Economic Forum, it is disrupting almost every industry in every country.

We stand on the brink of a technological revolution that will fundamentally alter the way we live, work, and relate to one another. In its scale, scope, and complexity, the transformation will be unlike anything humankind has experienced before. Klaus Schwab,Founder and Executive Chairman, World Economic Forum Geneva

Artificial intelligence is a conglomeration of concepts and technologies that means different things to different people self-driving cars, robots that impersonate humans, machine learning, and more and its applications are everywhere you look. The typical definition of AI looks something like this:

Source: KDNuggets

Jeremy Achin, CEO of DataRobot, defines AI more simply:

An AI is a computer system that is able to perform tasks that ordinarily require human intelligence. These artificial intelligence systems are powered by machine learning. Many of them are powered by machine learning, some of them are powered by specifically deep learning, some of them are powered by very boring things like just rules.

For a more in-depth explanation, watch Jeremys keynote on the subject from the Japan AI Experience.

Artificial intelligence systems are critical for companies that wish to extract value from data by automating and optimizing processes or producing actionable insights. Artificial intelligence systems powered by machine learning enable companies to leverage large amounts of available data to uncover insights and patterns that would be impossible for any one person to identify, enabling them to deliver more targeted, personalized communications, predict critical care events, identify likely fraudulent transactions, and more.

Harvard Business Review gives key insight into how important AI is in todays economic environment:

The effects of AI will be magnified in the coming decade, as manufacturing, retailing, transportation, finance, healthcare, law, advertising, insurance, entertainment, education, and virtually every other industry transform their core processes and business models to take advantage of machine learning.

Companies that fail to adopt AI and machine learning technologies are fated to be left behind:

DataRobot was founded on the belief that emerging AI and machine learning technologies should be available to all enterprises, regardless of size and resources. Thats why we invented automated machine learning, which allows users of all skill levels to easily and rapidly build and deploy machine learningmodels.

DataRobot believes in the democratization of AI, and for that reason, we developed a platform to enable business users across organizations to gain actionable, practical insights that result in tangible business value. DataRobot makes the power of AI accessible to users throughout your business, helping your organization transform into an AI-driven enterprise.

Delivering ROI for your artificial intelligence initiatives

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What Is Artificial Intelligence | Artificial Intelligence Wiki