Wendy McElroy: The Narrative and Philosophy of Cryptocurrency – Bitcoin News

The central banks of Britain, Japan, the euro zone, Sweden and Switzerland have grouped up to assess potential use cases for digital currencies. Talk of such currencies gained momentum after Facebook announced plans last year to introduce a cryptocurrency called libra, CNBC. In the light of such developments, it is evident that those who view crypto as an engine of freedom are losing control of the narrative.

Also read: Why User Experience Is Cryptos True Killer App

The narrative is an important concept because those who command the narrative are most likely to determine the outcome. Once closely associated with political correctness, the term has gone mainstream in recent years. The narrative is the story of somethingan issue, an ideabut it is more than merely relating the facts of a matter. In postmodern philosophy, from which political correctness draws heavily, the narrative creates reality; it creates the facts. The dominant story becomes the culture and the truth of a society. In other words, the narrative defines reality, not vice versa. This is one reason why the left is so preoccupied with the control of words and ideas; words and ideas control reality itself.

Most people use the narrative in a more casual way to mean a story that takes a specific approach or tone. Left-wing and right-wing narratives war with each other on issues, for example. Nevertheless, the term retains some of its original meaning. Giving context and interpretation to an issue does define what people view as true about it. In turn, the general publics perception does influence the events or facts that follow, especially in the absence of a competing narrative. This is why states censor: they want to eliminate competing truths.

This process applies to crypto, including the blockchain. The narrative of freedom can define the outcome. When it becomes effective at doing so, censorship is likely; at the moment, there is no need. Again, those to whom crypto is an engine of freedom are losing control of the narrative. Few things are as important to the future of crypto than to reclaim Bitcoins original vision of financial freedom from what is becoming the dominant context and interpretation: statism.

Happily, freedom enjoys a distinct advantage. The mechanics of crypto favor it strongly. Cryptos decentralization gives economic power to the average person who transfers wealth around the globe at will, requiring only the protection of solid encryption. And, yet, the state could win; some believe it already has.

Crypto needs a powerful competing narrative of freedom. It needs to remember its roots. Much more than financial freedom is at stake: every other freedom rests upon the ability of people to control their own wealth. Every time some aspect of free-market crypto is explored, such a narrative expands and users move closer to independence.

The first step in establishing a narrative of freedom is to reject the claim that crypto is simply another investment or money-making tool. Certainly, this is one function of crypto. And for some people, it may be the only function. But this is a comment upon their psychology or motives, not upon the inherent nature of crypto which exists as a thing apart. The claim is also dangerous; it opens the door to state control because the vast majority of financial institutions are now under its authority in one form or another and using them tends to legitimize their existence. This is a story that needs to change.

By far, the best freedom narrative for crypto is the truth because it withstands scrutiny and has the practical advantage of being backed by reality. The best approach to this narrative is to state the basics of crypto, simply and clearly. And then aggressively build upon them.

Crypto is usually discussed in economic, political, or technical terms. But Aristotle claimed that all things are philosophical. That is, the foundation of everything, including technology, is philosophical because philosophy asks the most fundamental questions about a thing.

Philosophy is not arcane or elite. Classical Greek philosophy used to serve the same function that psychology does today; it taught the principles of how to live a better life. Philosophy can be broken into three broad categories: metaphysics, epistemology, and ethics. Metaphysics deals with the first principles or nature of reality and the relationship between what exists, including abstractions. Epistemology is the theory of human knowledge, especially its acquisition, validation, and scope. Ethics is the branch of knowledge that addresses the moral principles governing behavior. Three questions capture the relationship between these categories. What exists? How do I know it? So what?

The Philosophy of Crypto is a book-length project but a brief glimpse of it can be garnered by loosely applying the three categories of philosophy to crypto.

Metaphysics. Metaphysics arises every time someone accuses crypto of not being real because it is based on nothing. This is a metaphysical attack as much as an economic or political one.

These days, the accusation is not generally hurled at the blockchain which has been widely adopted by businesses and states. The blockchains elegant efficiency means that it will continue to spread into every corner of life. And useful things automatically acquire the status of real.

The second half of cryptothe coinsis a different matter. Crypto without physical backing, such as gold or a basket of fiat currencies, is often called unreal. Clearly, this claim is untrue. At its root, crypto is an algorithma string of computer commands that produce a result. In this case, the result is a coin that is accepted as a medium of exchange. Whether or not people credit it as valid money, crypto is definitely real. As with fiat, its value is based upon peoples acceptance of it. Unlike fiat, the acceptance does not have to be coerced.

In his essay Bitcoin Equals Freedom, Ross Ulbricht pointed to another value upon which the something of crypto is basedfreedom from financial authorities, especially from central banks.

It is like magic that Bitcoin could somehow come from nothing, and without prior value or authoritative decree, become money. But Bitcoin did not appear in a vacuum. It was a solution to a problem cryptographers had been struggling with for many years: How to create digital money with no central authority that couldnt be forged and could be trusted.

Epistemology. What does truth mean in crypto, and how do human beings know it? The truth of crypto and the blockchain is that they work. The better they function, the truer they become. Human beings know when crypto and the blockchain are true because they work. Every time the blockchain delivers and preserves information, it is akin to a proof of principle.

Ethics. The so what? of crypto is contained within its structure. Which is to say, the ethics of crypto is an extension of its reality (metaphysics) and how its truth works (epistemology). Crypto is inherently decentralized and entirely voluntary. More than this, the blockchain cannot be centralized and controlled by a single hand or authority, and no one can be forced to use it. Free-market crypto is controlled by individual users who agree to exchange and co-operate to mutual advantage. It is a pure expression of non-violence. This is its ethical basis.

The only way to introduce violence is through crime, such as hacking a wallet. Overwhelmingly, the crime introduced is state control; even then, however, the state cannot impose its will on the blockchain, only on the people who use it. These people need to understand the narrative of freedom.

Ulbrichts article concludes, The promise of freedom and the allure of destiny energized the early community. Bitcoin was consciously, yet spontaneously taken up as money while no one was watching, and our world will never be the same.

Bitcoin was created to fulfill a promise of freedom and the allure of destiny. It was forged by cryptographers who did not know it would become a popular currency and investment. Its worth as money should never be denigrated, but those who view crypto only as money are missing the point. The narrative of freedom must do a better job of explaining.

Op-ed disclaimer: This is an Op-ed article. The opinions expressed in this article are the authors own. Bitcoin.com is not responsible for or liable for any content, accuracy or quality within the Op-ed article. Readers should do their own due diligence before taking any actions related to the content. Bitcoin.com is not responsible, directly or indirectly, for any damage or loss caused or alleged to be caused by or in connection with the use of or reliance on any information in this Op-ed article.

Images courtesy of Shutterstock.

Did you know you can verify any unconfirmed Bitcoin transaction with our Bitcoin Block Explorer tool? Simply complete a Bitcoin address search to view it on the blockchain. Plus, visit our Bitcoin Charts to see whats happening in the industry.

Wendy McElroy is a Canadian individualist anarchist and individualist feminist. She was a co-founder of the Voluntaryist magazine and modern movement in 1982, and has authored over a dozen books, scripted dozens of documentaries, worked several years for FOX News and written hundreds of articles in periodicals ranging from scholarly journals to Penthouse. She has been a vocal defender of WikiLeaks and its head Julian Assange.

Continued here:
Wendy McElroy: The Narrative and Philosophy of Cryptocurrency - Bitcoin News

Bitcoin Price Analysis: The Calm Before The Storm? BTC Continues To Trade Inside a Very Tight Range – CryptoPotato

On our recent price analysis from two days ago, we had anticipated a huge price move that was supposed to be coming soon.

As can be seen, shortly after our analysis, there was the opening of the Chinese stock market after the long Lunar vacation. At the same time, Bitcoin spiked $400 to a new 2020 high over $9600 but quickly retraced.

Since then, Bitcoin is slowly forming a bull-flag on the 4-hour chart. The coin is trading inside a descending channel (or flag). Keep in mind, that kind of flag tends to break to the upper side. However, there is always the chance to break to the downside as well.

Bitcoin had seen dull price action over the past days, and overall, the trading range for the past week had been mostly between $9180 to $9400, which is as little as a 2% trading range. Quite stable and very odd for Bitcoin, compared to the current volatility of the stock markets, and Tesla in particular.

The Golden cross: As we can see on the following daily chart, Bitcoin is having a mini Golden cross, as the 50-days moving average line (pink) crosses above the 100-days line (white). As mentioned here before, the real and more serious cross involved the 50 and the 200 MA (light green) lines. However, some analysts also give some respect to this 50 on 100 Golden cross.

Total Market Cap: $258.5 billion

Bitcoin Market Cap: $168 billion

BTC Dominance Index: 65.2%

*Data by CoinGecko

Support/Resistance levels: Bitcoin is now testing the MA-50 supporting line on the 4-hour chart, along with the $9180 $9200 resistance turned support level. In general, the coins short-term is dependent on the bull-flag on the 4-hour chart. A breakout will likely lead to the next price moves direction.

Below $9180, the next level of support is at $9075, which is the 38.2% Fibonacci retracement level of the recent bullish move.

Further below lies $9000 and $8900, along with the significant 200-days moving average line. The next level is the Golden Fib level 61.8% at $8770.

From the bullish side, the first level of resistance is now the upper angle of the flag, roughly around $9300. Further above lies the $9450 area (a recent high) before the 2020 high at $9550 $9600.

The RSI Indicator: As mentioned here on the previous analysis, there was more room to go down on behalf of the RSI indicator. As of now, the indicator is getting very close to a crucial ascending trend-line support (~60 RSI levels).

Trading volume: After a week of declining volume, finally, the volume started to accumulate once again. For the past three days, the volume is rising; however, these are still relatively low volume candles.

Enjoy reading? Please share:

Disclaimer: Information found on CryptoPotato is those of writers quoted. It does not represent the opinions of CryptoPotato on whether to buy, sell, or hold any investments. You are advised to conduct your own research before making any investment decisions. Use provided information at your own risk. See Disclaimer for more information.

Cryptocurrency chartsby TradingView.

See the original post here:
Bitcoin Price Analysis: The Calm Before The Storm? BTC Continues To Trade Inside a Very Tight Range - CryptoPotato

Three Crypto Assets Surged More Than 175%, Beating Bitcoin, As Altcoin Market Exploded in January – The Daily Hodl

January was a stellar month for most crypto assets. The total market cap of the overall crypto market jumped 35%, from $193 billion to $255 billion.

A new report from Binance Research highlights the strength of the altcoin market which excludes Bitcoin throughout the month. It grew from $61 billion to $86 billion, posting gains of over 40%. The increase in the altcoin market cap led to a 3% drop in Bitcoins market dominance, from 68% down to 65%.

Spearheading the altcoin charge were two mid-cap coins Dash (DASH) and ZCoin (XZC). Dash surged by 181% last month while ZCoin was not far behind with a 179% spike. Binance excluded the fifth-largest cryptocurrency, Bitcoin SV (BSV), which does not trade on the exchange and rallied 177% in January.

Bitcoin Cash surged by 85.2%, followed by Litecoin and Cardano. Both coins climbed by 65.1% last month, leading the pack of several other large-cap cryptocurrencies that turned green.

Binance Research is also revealing insight on how its institutional clients played the market throughout the month.

The company says its institutional trading desk saw a high number of buy flows for mid-to-low cap altcoins at the start of the month. That fell off by mid-January as traders began to buy Bitcoin. By the end of the month, Binance says fewer traders sold their altcoin positions, realizing that many altcoins were performing as well as if not better than BTC.

Featured Image: Shutterstock/ymcgraphic

See more here:
Three Crypto Assets Surged More Than 175%, Beating Bitcoin, As Altcoin Market Exploded in January - The Daily Hodl

Ripple Soars as Bitcoin and Altcoins Drift – FXStreet

The last 24 hours have seen Ripple (+10%) soar over $0.277, setting its highest price in months whereas Bitcoin(-0.18%) is still looking for buyers with its dominance down to 64.6 percent. Ethereum (+1.52%) is a bit more bullish, and NEO (+5.78%), Stellar Lumens (+7.26%, DogeCoin (+6.49%), and Lisk (+15.5%) were joining Ripple's party.

The Ethereum tokens had a mixed day, with LINK (+1.92%), LEO (+1.56%) and CRO ( +1.51$) recovering, HEDG (-1.87%) and MKR (-1.83%) and REP (-3.87%) shedding off some gains.

The crypto sector Market cap is currently $261.165 billion, up by 0.5 percent from yesterday, while the traded volume in the last 24 hours was $40.2 billion, up 12.65 percent from yesterday's value.

Ripple is having a great time. Yesterday, BitMEX launched a new perpetual swap contract tied to XREP (XRPUSD) that moved its traded volume to the highest places and its price rocketing. But, maybe it's not just fireworks. Also last Tuesday, Ripple announced its partnership with International Money Express Inc (Intermex), a leading remittance services firm that is primarily focused in the Caribean and Latin America. "The partnership will enable Intermex to leverage RippleNet for faster, transparent cross-border remittance services between the United States and Mexico," - said Ripple's press release.

Intercontinental Exchange (ICE), which owns the New York Stock Exchange (NYSE) and the Bitcoin Futures trading Bakkt, has made a formal offer to buy eBay for $30 billion, as reported by the Wall Street Journal.

Bitcoin has recovered above its $9,240 level, although it is still moving unconvincing. The MACD is turning up and is ready to make a bullish crossover, but its price still moves in the lower side of the Bollinger bands and below its 50-period SMA. The Key level to observe is $9,100 to the downside and $9,340 to the upside. The good news is the descending trendline has been broken, which might mean the bears don't have the strength to continue pushing it further down.

Support

Pivot Point

Resistance

9,100

9,225

9,340

8,970

9,550

8,760

9,700

As we guessed yesterday, the triangular pattern was a continuation formation, and the triangle broke sharply to the upside. As we see on the chart, the price moves very close to the +3SD Bollinger line, which shows the strength of XRP's bullish momentum. As we can see, the price stopped its upward advance at $0.283 and is currently retracing. That is fine since it is overbought. So profit-taking and short-term sellers should drive it to the vicinity of the +1 SD. Thus, we expect a consolidation of the price in this area, possibly retracing close to the $0.274 level.

Support

Pivot Point

Resistance

0.2660

0.2660

0.2740

0.2600

0.2830

0.2550

1602.48

Ethereum also broke the triangular formation to the upside, and the price still follows the lower trendline of its ascending channel. The MACD is turning up and soon to make a bullish crossover. Finally, the price has moved to the upward side of the Bollinger bands and close to its +1SD line. These are all signs of a continuation of the bullish trend. The price is now at $190, and the next resistances to break are $191.5, $196 and $200.

Support

Pivot Point

Resistance

185.00

187.00

191.50

180.00

196.00

177.00

199.00

Litecoin also broke its triangular pattern to the upside, its price following the upward trendline. Also, the MACD is very close to making its bullish crossover. The trend is up, but the action is still on the lower side of the Bollinger bands, and the $70 level is to be broken. So, buyersshouldwaitforthis to happen.

Support

Pivot Point

Resistance

67.00

68.40

70.00

64.50

73.00

62.50

75.00

Try Secure Leveraged Trading with EagleFX!

Here is the original post:
Ripple Soars as Bitcoin and Altcoins Drift - FXStreet

Speechmatics and Soho2 apply machine learning to analyse voice data – Finextra

Speechmatics and Soho2 have today announced their partnership to deliver consulting services to their customers, and a new product offering Speech2.

Soho2 has significant depth in delivering machine-learning driven solutions to market. The new product from Soho2 will give companies in legal, compliance and contact centers the invaluable ability to analyze voice data garnered from calls. Speech2 enables companies to bring new levels of flexibility to data analysis for high-volume, real time or recorded voice data through mission-critical, accurate speech recognition.

Using AI and machine learning, the solution will deliver an unparalleled ability to derive insight from voice data and also manage risk. The product can be deployed in any customer-managed environment to enable control over personal or sensitive data to be retained.

As part of the new product offering, Speechmatics - a UK leader in any context speech recognition technology - will transcribe voice data into accurate, contextual understanding for analysis. Speech2 will allow businesses to identify and address risks, as well as pinpoint missing sales opportunities. The product can also identify cases of fraud, while the legal industry can identify risks with the data, and even aid with event reconstruction.

George Tziahanas, Managing Partner of Soho2, said: Our experience demonstrates the potential for great innovation in machine learning, delivering huge commercial value to enterprises across industries. We teamed up with Speechmatics to ensure our latest services and product deliver the best speech recognition technology on the market. The partnership enables us to innovate with voice securely which is crucial to our customers and industries.

Jeff Palmer, VP of Sales at Speechmatics, added: Speech2 will deliver unparalleled insights and risk management abilities, using Speechmatics any-context speech recognition engine. Soho2 also brings depth in services that deliver high-value machine learning solutions, which will benefit their customer-base. Were excited to be working with Soho2 and seeing how their customers derive value from their voice data and view it with a renewed sense of curiosity.

More here:
Speechmatics and Soho2 apply machine learning to analyse voice data - Finextra

Top Machine Learning Projects Launched By Google In 2020 (Till Date) – Analytics India Magazine

It may be that time of the year when new year resolutions start to fizzle, but Google seems to be just getting started.The tech giant has been building tools and services to bring in the benefits of artificial intelligence (AI) to its users. The company has begun upping its arsenal of AI-powered products with a string of new releases this month alone.

Here is a list of the top products launched by Google in January 2020.

Although first introduced in 2014, the latest iterations of sequence-to-sequence (seq2seq) AI models have strengthened the capability of key text-generating tasks including sentence formation and grammar correction. Googles LaserTagger, which the company has open-sourced, speeds up the text generation process and reduces the chances of errors

Compared to traditional seq2seq methods, LaserTagger computes predictions up to 100 times faster, making it suitable for real-time applications. Furthermore, it can be plugged into an existing technology stack without adding any noticeable latency on the user side because of its high inference speed. These advantages become even more pronounced when applied at a large scale.

The company has expanded its Coral lineup by unveiling two new Coral AI products Coral Dev Board Mini and Coral Accelerator Module. Announced ahead of the Consumer Electronics Show (CES) this year, the latest addition to the Coral family followed a successful beta run of the platform in October 2019.

The Coral Accelerator Module is a multi-chip package that encapsulates the companys custom-designed Edge Tensor Processing Unit (TPU). The chip inside the Coral Dev Board is designed to execute multiple computer vision models at 30 frames per second or a single model at over 100fps. Users of this technology have said that it is easy to integrate into custom PCB designs.

Coral Accelerator Module, a new multi-chip module with Google Edge TPU.

Google has also released the Coral Dev Board Mini which provides a smaller form-factor, lower-power, and a cost-effective alternative to the Coral Dev Board.

Caption: The Coral Dev Board Mini is a cheaper, smaller and lower power version of the Coral Dev Board

Officially announced in March 2019, the Coral products were intended to help developers work more efficiently by reducing their reliance on connections to cloud-based systems by creating AI that works locally.

Chatbots are one of the hottest trends in AI owing to its tremendous growth in applications. Google has added to the mix with its human-like multi-turn open-domain version. Meena has been trained in an end-to-end fashion on data mined from social media conversations held in the public domain with a totalling 300GB+ text data. Furthermore, it is massive in size with 2.6B parameter neural network and has been trained to minimize perplexity of the next token.

Furthermore, Googles human evaluation metric called Sensibleness and Specificity Average (SSA) also captures the key elements of a human-like multi-turn conversation, making this chatbot even more versatile. In a blog post, Google had claimed that Meena can conduct conversations that are more sensible and specific than existing state-of-the-art chatbots.

Plugged as an important development of Googles Transformer the novel neural network architecture for language understanding Reformer is intended to handle context windows of up to 1 million words, all on a single AI accelerator using only 16GB of memory.

Google had first mooted the idea of a new transformer model in a research paper in collaboration with UC Berkeley in 2019. The core idea behind this model was self-attention, and the ability to attend to different positions of an input sequence to compute a representation of that sequence elaborated in one of our articles.

Today, Reformer can process whole books concurrently and that too on a single gadget, thereby exhibiting great potential.

Google has time and again reiterated its commitment to the development of AI. Seeing it as more profound than fire or electricity, it firmly believes that this technology can eliminate many of the constraints we face today.

The company has also delved into research anchored around AI that is spread across a host of sectors, whether it be detecting breast cancer or protecting whales or other endangered species.

comments

Read the original post:
Top Machine Learning Projects Launched By Google In 2020 (Till Date) - Analytics India Magazine

Reinforcement Learning: An Introduction to the Technology – Yahoo Finance

NEW YORK, Feb. 3, 2020 /PRNewswire/ --

Report Includes:- A general framework for deep Reinforcement Learning (RL) also known as a semi-supervised learning model in machine learning paradigm

Read the full report: https://www.reportlinker.com/p05843529/?utm_source=PRN

- Assessing the breadth and depth of RL applications in real-world domains, including increased data efficiency and stability as well as multi-tasking- Understanding of the RL algorithm from different aspects; and persuade the decision makers and researchers to put more efforts on RL research

Reasons for Doing This Report:These days, machine learning (ML), which is a subset of computer science, is one of the most rapidly growing fields in the technology world.It is considered to be a core field for implementing artificial intelligence (AI) and data science.

The adoption of data-intensive machine learning methods like reinforcement learning is playing a major role in decision-making across various industries such as healthcare, education, manufacturing, policing, financial modelling and marketing.The growing demand for more complex machine working is driving the demand of learning-based methods in the ML field.

Reinforcement learning also presents a unique opportunity to address the dynamic behavior of systems.This study was conducted in order to understand the current state of reinforcement learning and track its adoption along various verticals, and it seeks to put forth ways to fully exploit the benefits of this technology.This study will serve as a guide and benchmark for technology vendors, manufacturers of the hardware that supports AI, as well as the end users who will finally use this technology.

Decisionmakers will find the information useful in developing business strategies and in identifying areas for research and development.

Read the full report: https://www.reportlinker.com/p05843529/?utm_source=PRN

About Reportlinker ReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

__________________________ Contact Clare: clare@reportlinker.com US: (339)-368-6001 Intl: +1 339-368-6001

Story continues

View original content:http://www.prnewswire.com/news-releases/reinforcement-learning-an-introduction-to-the-technology-300997487.html

SOURCE Reportlinker

Here is the original post:
Reinforcement Learning: An Introduction to the Technology - Yahoo Finance

The Role of AI and Machine Learning in Cybersecurity – Analytics Insight

AI and machine learning are the kind of buzzwords that generate a lot of interest; hence, they get thrown around all the time. But what do they actually mean? And are they as instrumental to the future of cybersecurity as many believe?

When a large set of data is involved, having to analyze it all by hand seems like a nightmare. Its the kind of work that one would describe as boring and tedious. Not to mention the fact it would take a lot of staring at the screen to find what youve set out to discover.

The great thing about machines and technology is that unlike humans it never gets tired. Its also better geared for being able to notice patterns. Machine learning is what you get when you reach the point of teaching your tools on how to spot patterns. The AI helps you interpret it all better and make the solution self-sufficient.

Cybersecurity solutions (antivirus scanners in particular) are all about spotting a pattern and planning the right response. These scanners rely on heuristic modeling. It gives them the ability to recognize a piece of code as malicious, even though it might be the case that no one has flagged it as such before. In essence, it has plenty to do with teaching the software to recognize and alert you when something is out of the ordinary.

As soon as something oversteps the threshold of tolerance, it triggers an alarm. From there on out, the rest is up to the user. For instance, the user may instruct the antivirus software to move the infected file to quarantine. It can do so with or without human intervention.

Applying AI to cybersecurity solutions is taking things up a notch. Without it, the option of having the software learn on its own by observing would not be possible.

Imagine having an entity working in the background that knows you so well that it can predict your every move. It might be slight nuances. For example, the way you move your mouse or the parts of the web youre browsing on a frequent basis. Even the order of the applications you launch upon logging in.

Without having to introduce yourself, the AI would get to know you and your habits pretty well. Thus, it would form a digital fingerprint of you. It sounds scary, but it could come in handy. For instance, it could raise the alarm if an unauthorized individual ever gets access to your PC.

Of course, observing your behavior is not the end of what employment of AI and machine learning can do. Why not do the same thing for computer processes?

Imagine having to monitor what programs are running in the background yourself. Tracking how much resources they consume all day, every day, by hand. It doesnt sound enjoyable now, does it? But its the work AI excels at.

Without lifting a finger, youd have a powerful watchdog that would start barking as soon as something is out of the ordinary. For instance, it could alert you about malicious operating system behaviors. You would know right away about crypto mining malware or other types of threats affecting your computer.

The smart malware designers make it so that your systems CPU usage gets off the charts only when youre not using the PC. Theres no way to spot such a thing while youre away from the keyboard. Unless you have AI-powered cybersecurity solutions to track it all for you 24/7.

Webmasters keep trying to fend off bot traffic and automated scripts. These are used for automatic data scraping and similar activities. For instance, someone could write a script to harvest every bit of contact details on the website. They can then send unsolicited offers to all those contacts. Even when they dont scrape contacts, no one wants bot traffic because it consumes valuable server resources and slows everything down for legitimate browsers. Thus, it harms the user experience.

The simple solution is to block a range of IP addresses. But by using a VPN (you can read more about it here) server or a proxy, a script can get around the obstacle. Now lets introduce some AI into the equation. By observing every browsers activity, it would be able to recognize repetitive behavior. It would associate it with an IP address thats currently browsing, then flag it. Sure, a script may discard an IP address and try with a new one. But the fingerprint left by its activities would remain since its rather much pattern-based. In the end, the new IP could be flagged much faster by automated observation.

Since they came to be, AI and machine learning have changed the world of cybersecurity forever. As time goes on, they will keep getting more and more refined. Its a matter of question when it will reach the point of becoming your cybersecurity watchdog, tailored to your needs.

The rest is here:
The Role of AI and Machine Learning in Cybersecurity - Analytics Insight

Jobs of the Future: The Hottest Areas of Tech Education Dallas Innovates – dallasinnovates.com

Dallas-Fort Worth has more than 40 higher learning institutions, each with programs that incorporate on-the-job training and monitor the pulse of our rapidly changing technological landscape.

While North Texas houses tons of major corporations, another factor that makes it so formidable is the hub of learning establishments that have long called the area home. Fortunately, with so many local universities, North Texas has fostered a collection of pioneer programs in high-level facilities that monitor the pulse of our rapidly changing technological landscape.

These programs create an employment pipeline that funnels the best and brightest to top companies across the stateand beyond. Heres a few of them.

At the University of Texas at Dallas, the Center for Applied AI and Machine Learning, within the Department of Computer Science, focuses on applying artificial intelligence and machine learning to create viable industry solutions and educate the next generation of scientists. Upon graduation, students often go into finance, logistics, healthcare, or software security and companies where they end up might include Amazon, Facebook, Google, Microsoft, AT&T, JPMorgan Chase, Samsung, Dell, and many others, says Sriraam Natarajan, associate professor and director of the Center for Machine Learning.

While the Center for Applied AI and Machine Learning functions as a service arm that provides opportunities for students, the Center for Machine Learning functions as a research arm, facilitating faculty members development of algorithms while supporting educational activity and community outreach.

We have a great faculty, and on top of that, were expanding the program, Natarajan says. Were looking at the most recent trends in machine learning, and aligning our courses so that they match what the industry needs.

The Department of Information Science at UNT, [emailprotected], and the Department of Computer Science & Engineering at UTA also offer courses, specializations, and concentrations in machine learning and artificial intelligence that help train students for cutting-edge jobs including software engineering, data engineering, application development, and programming.

Software engineering is one field that opens doors in nearly every industry across the globe.

Every company is a tech company, says Duane Dankesreiter, Senior Vice President of Research and Innovation for the Dallas Regional Chamber. You may be a big bank, but you need data analytics and software engineering to succeed.

At the University of Texas at Arlington, software engineering students collaborate with local companies to hone their skills for the future while, at the University of North Texas, students test their mettle by solving practical problems. The Erik Jonsson School of Engineering and Computer Science at the University of Texas at Dallas is ranked eighth in the country for software engineering research, and boasts cutting-edge forays into semiconductor design, wireless networking, organic electronics, and medical imaging. The schools internship and cooperative education program places 12,000 students at local tech companies annually.

With the modern world inextricably linked to technology, information security will only become more important as the future races forward and these concerns are top-of-mind for everyone from corporate leaders to everyday individuals. The Cyber Security Research and Education Institute at the University of Texas at Dallas is poised to address these concerns by conducting advanced cybersecurity research and providing inclusive education and training to enable the next generation of cyber security professionals to respond to the cyber threats of tomorrow. Additionally, faculty at Texas A&M University-Commerce is working to solve issues and concerns that revolve around resilience, risk awareness, and cyber-physical security.

Art and tech overlap more with each passing day. SMUs Simmons School of Education and Human Development utilizes augmented reality and virtual reality in its teaching projects. The University of Texas at Dallas houses one of the few motion capture and virtual reality laboratories in the country, and its applications range from gaming to military training scenarios to education and medical research. At UTDs ATEC School, students incorporate AR and VR into their capstone projects, like the fashion photographer who created an interactive magazine. The photos were digitally coated, and readers could use their phones to access video footage of the fashion models discussing their #metoo moments.

Augmented reality is emerging as a new and popular media form, Balsamo says. It involves new technologies, so our faculty and students are investigating how we can use AR in interesting ways that help us as human beings.

A pressing need for medical advancements in various disciplines is an expected side effect of a climbing population and across-the-board increased life expectancy. The University of Texas Southwestern Medical Center conducts research across a variety of fields, including cancer, heart disease, and neuroscience. With an award-winning staff, more than 200 laboratories, and annual funding of around $470 million, the Center trains around 3600 health professionals every year.

In the Biology Department at Texas Womens University, researchers conduct pioneering investigations into pain management, and at the University of North Texass Bio Discovery Institute, researchers work with bio-based materials to discover their potential applications in construction, transportation, and healthcare. The UNT Health Science Center contains six schools that tackle forward-looking disciplines, like forensic genetics and Alzheimers research. Notable is UNTHSCs School of Medicine, a joint collaboration with Texas Christian University that aims to create empathetic, globally conscious medical leaders.

A version of this story was originally published in Dallas Innovates: The [Tech] Talent Issue.

Dallas Innovates: The [Tech] Talent Issue, a special edition of the Dallas Innovates Magazine, looks at how companies in Dallas-Fort Worth are attracting and retaining the best talent. Startups, corporates, nonprofits, and organizations work hard to create a strong culture, promote diversity, and implement training programs that can help achieve success.

Sign up to keep your eye on whats new and next in Dallas-Fort Worth, every day.

Musings on innovation from the region's paradigm-shifting companies and organizations.

A new generation of innovators is taking its place in Dallas-Fort Worth lore, creating the next wave of great companies, services, and ideas.

No matter which of our six buckets you look atstartup, enterprise, invention, education, innovation, or creativeyou're sure to find leaders capable of disrupting industries, benefiting society, or changing the world.

Dallas Innovates, Every Day: Here's your briefing on ideas and innovation in North Texas.

From Toyota's city of the future to Ericsson's connected cars, AT&T's 5G phone lineup to Polte's tracking devices, the region was well-represented at this year's annual Consumer Electronics Show.

Continue reading here:
Jobs of the Future: The Hottest Areas of Tech Education Dallas Innovates - dallasinnovates.com

Databricks opens major engineering centre in Toronto why that’s a big deal for Canada – IT World Canada

In todays world where the competition is fierce for talent, it says a lot when your country is selected for opening a major engineering centre. It says, even more, when that company is a global leader in bringing the power or AI and machine learning to the enterprise. As such, upon hearing about Databricks coming to Canada, it sparked my interest to learn more.

Databricks is leading the charge for organizations to derive value out of AI and machine learning and is one of the fastest-growing SaaS companies in the world today. The next decade of innovation will combine the technology domains of cloud, data, and AI Databricks is sitting at the intersection of all three.

Databricks was founded in 2013 and has thousands of globalcustomersincluding Comcast, Shell, HP, Expedia, and Regeneron among many others across virtually every industry. Databricks is currently valued at over $6B with funding from leading investors like Andreessen Horowitz and NEA. To help bring the power of AI to the enterprise, Databricks also has hundreds of globalpartnersthat include Microsoft, Amazon, Tableau, Informatica, Cap Gemini and Booz Allen Hamilton.

Interestingly, you could say that Canada is actually embedded in the DNA of Databricks as the Co-founder and Chief Architect, Reynold Xin, is a University of Toronto alum. Reynold has BASc in Engineering from the U of T and holds a Ph.D from the University of California, Berkeley. Additionally, co-founder and chief technologist, Matei Zaharia, grew up in Toronto, went to the University of Waterloo and has a Ph.D. in Computer Science from the University of California, Berkeley. I connected with Reynold to gain further insight into the company, the Canada decision, and what the technical vision of the future may hold for AI and machine learning enabling organizations to make data-driven decisions from improved health outcomes to superior operational efficiency.

Brian Clendenin: For those that may not know, what is Databricks?

Reynold Xin: Databricks is a 6-year-old technology startup based in San Francisco. Our mission is to help data teams solve the worlds toughest problems, from security threat detection to cancer drug development. We do this by building and running the worlds best data and AI infrastructure platform, so our customers can focus on the high-value challenges that are central to their own missions.

The founding team were the original creators of Apache Spark. We worked on research problems in big data and machine learning at UC Berkeley. As part of that, we had a very close collaborative relationship with Silicon Valley, and saw some of the earlier use cases and challenges with data. We created Databricks with the belief that data has the potential to help solve some of the worlds toughest problems.

Fast forward six years, the company has evolved into a global organization with over 1000 employees and thousands of organizations entrust us with their most critical data infrastructure. Last year, we announced a $400 million Series F round of funding which valued the company at $6.2 billion USD.

Brian: Why select Canada to open a global engineering centre?

Reynold: Our secret sauce is the people at Databricks. We want to find the most talented and motivated people and create success collectively. We started in the San Francisco Bay Area, which has the highest concentration of software engineers. But the demand for our platform is so large that we need to grow the team substantially.

As part of our quest for talent, we opened our European Development Center in Amsterdam three years ago. The Amsterdam office has become an integral part of the Databricks innovation factory. They have shipped some of the highest impact features that made our customers life so much better.

Earlier this year, we decided its time to repeat the success we had seen with Amsterdam, and set out to find our third engineering hub. This time, we started with the following criteria:

It wasnt that difficult to narrow it down to Toronto, especially considering two of the founders have ties to Toronto. Matei grew up in Toronto, and I went to college at U of T.

Brian: How do you envision Canadians will contribute to Databricks innovation and market leadership?

Reynold: Throughout modern history, Canadians have played a critical part in the invention of new technologies, from medicine to more recently information technology. But at the same time, theres also a large brain drain of Canadians going south to the United States, often for better pay or better work.

We want to create an awesome environment in Toronto so the most talented engineers can work on the cutting edge technologies that have massive real-life impacts. They should wake up every day eager to come into work, knowing that the technologies they are building have contributed to fundamental societal issues such as reducing traffic congestion or curing cancer.

It is what they will be building that will define the next decade for Databricks, as part of our goal to enable every organization to leverage data and solve the toughest problems.

In Amsterdam, in addition to hiring a lot locally, weve also attracted some of the best engineers in other parts of the world and convinced them to move to the Netherlands. I think we will be able to help Toronto attract this calibre of people over as well.

Brian: Youve mentioned that Databricks is at the intersection of cloud computing, big data, and machine learning. Will these technology domains be the big drivers of innovation over the next decade?

Reynold: Absolutely, and Databricks is uniquely positioned at the intersection of these 3 megatrends. When we first started the company, we decided we wanted to build a cloud data platform that has diverse capabilities including machine learning. Most companies back then, and even now, are focusing on on-prem shrinkwrap software and on data warehousing, without any capabilities to do machine learning. Many investors we talked to were very skeptical about our approach: although big data was already big, the concept of cloud computing and machine learning was nascent and the market was small.

In 2020, its clear all of them took off and became megatrends. Cloud computing enables the rapid delivery of software as a service and compute resources on demand. This can create massive cost savings for IT infrastructure, but the real reason Im super excited about it is that it could shorten time-to-market for new applications our customers are developing from years to days.

As you know the field of machine learning isnt new, but whats completely new is the abundance of data available at our fingertips to train and apply state-of-the-art models. These models in return can help considerably enhance customer experiences, products, and help drive positive business outcomes. However, without computing power, without the ability to scale, processing big data or training machine learning models on big data becomes extremely challenging.

So it truly is the combination of the cloud, big data, and machine learning technologies combined will drive massive innovations over the next decade. And thats what we have been focusing on.

Brian: What is the promise of AI and machine learning in the enterprise?

Reynold: The promise of AI in the enterprise is massive.For the past three decades, data warehouses have become a standard component in any enterprise IT architecture. Those allow enterprises to look into the past, understanding how their businesses are doing. Thats obviously tremendously important and is phase 1 of the revolution.

We are on the verge of starting phase 2 with AI: look into (predict) the future.

Why is this important? Imagine what enterprises can do if they have a crystal ball into the future. To give you some examples. We have been working with Bechtel to reinvent the construction industry leveraging AI to sequence the complex dependency graph in billion-dollar construction projects. Weve worked with Regeneron in accelerating drug discovery, and Quby in helping homeowners reduce energy consumption.

However, few organizations have succeeded so far due to many challenges like infrastructure limitation, poor data quality, or challenges hiring qualified workforce in that space. We believe our technology can uniquely help solve many of the technical challenges, and we continue to add groundbreaking innovation to the platform based on customer needs. We partner with hundreds of ISVs and technology providers to allow customers to leverage their investments and for example, connect their existing infrastructure to the Databricks platform. In addition, we have and continue to scale as an organization, and our customer success and support organization work very closely with thousands of customers worldwide to help their data teams innovate faster.

Brian: What type of software engineering talent is optimal for Databricks?

Reynold: We are hiring software engineers from all subareas of computer science, from cloud infrastructure, databases, distributed systems, developer tooling, to machine learning. Our engineers are recognized by their peers outside Databricks as the top engineers, but at the same time are extremely collaborative and customer-obsessed. That means they tend to care a lot more about the impact of what they have created on our customers, rather than the creation process itself. We also emphasize own it a lot as a cultural principle. People are here on a mission and they are willing to do whatever it takes to drive projects end to end. When something is not going well, they dont spend energy blaming somebody else, but rather focusing on finding a solution.

Brian: What do you find most exciting about the future for Databricks?

Reynold: Of course one of the most exciting parts is the growth of the company. We have become one of the fastest-growing SaaS companies ever created, and it will be terrific to see the next phases of growth.

What I find even more exciting than the growth itself is I wake up every day learning new use cases that our platform has enabled our customers to do. We already discussed some very interesting ones that have already created a large impact, but I believe the best is yet to come. Perhaps one way we will indeed receive an email from a major pharmaceutical company or a university research lab that some data analysis and machine learning done on our platform has led to the creation of a new drug that cures cancer. We are really lucky that we are solving intellectually challenging technical problems every day, and those solutions are helping create a better world.

Excerpt from:
Databricks opens major engineering centre in Toronto why that's a big deal for Canada - IT World Canada