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Lost Passwords Lock Millionaires Out of Their Bitcoin Fortunes – The New York Times

Stefan Thomas, a German-born programmer living in San Francisco, has two guesses left to figure out a password that is worth, as of this week, about $220 million.

The password will let him unlock a small hard drive, known as an IronKey, which contains the private keys to a digital wallet that holds 7,002 Bitcoin. While the price of Bitcoin dropped sharply on Monday, it is still up more than 50 percent from just a month ago, when it passed its previous all-time high of around $20,000.

The problem is that Mr. Thomas years ago lost the paper where he wrote down the password for his IronKey, which gives users 10 guesses before it seizes up and encrypts its contents forever. He has since tried eight of his most commonly used password formulations to no avail.

I would just lay in bed and think about it, Mr. Thomas said. Then I would go to the computer with some new strategy, and it wouldnt work, and I would be desperate again.

Bitcoin, which has been on an extraordinary and volatile eight-month run, has made a lot of its holders very rich in a short time, even as the coronavirus pandemic has ravaged the world economy.

But the cryptocurrencys unusual nature has also meant that many people are locked out of their Bitcoin fortunes as a result of lost or forgotten keys. They have been forced to watch, helpless, as the price has risen and fallen sharply, unable to cash in on their digital wealth.

Of the existing 18.5 million Bitcoin, around 20 percent currently worth around $140 billion appear to be in lost or otherwise stranded wallets, according to the cryptocurrency data firm Chainalysis. Wallet Recovery Services, a business that helps find lost digital keys, said it had gotten 70 requests a day from people who wanted help recovering their riches, three times the number of a month ago.

Bitcoin owners who are locked out of their wallets speak of endless days and nights of frustration as they have tried to get access to their fortunes. Many have owned the coins since Bitcoins early days a decade ago, when no one had confidence that the tokens would be worth anything.

Through the years I would say I have spent hundreds of hours trying to get back into these wallets, said Brad Yasar, an entrepreneur in Los Angeles who has a few desktop computers that contain thousands of Bitcoin he created, or mined, during the early days of the technology. While those Bitcoin are now worth hundreds of millions of dollars, he lost his passwords many years ago and has put the hard drives containing them in vacuum-sealed bags, out of sight.

I dont want to be reminded every day that what I have now is a fraction of what I could have that I lost, he said.

The dilemma is a stark reminder of Bitcoins unusual technological underpinnings, which set it apart from normal money and give it some of its most vaunted and riskiest qualities. With traditional bank accounts and online wallets, banks like Wells Fargo and other financial companies like PayPal can provide people the passwords to their accounts or reset lost passwords.

But Bitcoin has no company to provide or store passwords. The virtual currencys creator, a shadowy figure known as Satoshi Nakamoto, has said Bitcoins central idea was to allow anyone in the world to open a digital bank account and hold the money in a way that no government could prevent or regulate.

This is made possible by the structure of Bitcoin, which is governed by a network of computers that agreed to follow software containing all the rules for the cryptocurrency. The software includes a complex algorithm that makes it possible to create an address, and associated private key, which is known only by the person who created the wallet.

The software also allows the Bitcoin network to confirm the accuracy of the password to allow transactions, without seeing or knowing the password itself. In short, the system makes it possible for anyone to create a Bitcoin wallet without having to register with a financial institution or go through any sort of identity check.

That has made Bitcoin popular with criminals, who can use the money without revealing their identity. It has also attracted people in countries like China and Venezuela, where authoritarian governments are known for raiding or shutting down traditional bank accounts.

But the structure of this system did not account for just how bad people can be at remembering and securing their passwords.

Even sophisticated investors have been completely incapable of doing any kind of management of private keys, said Diogo Monica, a co-founder of a start-up called Anchorage, which helps companies handle cryptocurrency security. Mr. Monica started the company in 2017 after helping a hedge fund regain access to one of its Bitcoin wallets.

Mr. Thomas, the programmer, said he was drawn to Bitcoin partly because it was outside the control of a country or company. In 2011, when he was living in Switzerland, he was given the 7,002 Bitcoin by an early Bitcoin fanatic as a reward for making an animated video, What is Bitcoin?, which introduced many people to the technology.

That year, he lost the digital keys to the wallet holding the Bitcoin. Since then, as Bitcoins value has soared and fallen and he could not get his hands on the money, Mr. Thomas has soured on the idea that people should be their own bank and hold their own money.

This whole idea of being your own bank let me put it this way: Do you make your own shoes? he said. The reason we have banks is that we dont want to deal with all those things that banks do.

Other Bitcoin believers have also realized the difficulties of being their own bank. Some have outsourced the work of holding Bitcoin to start-ups and exchanges that secure the private keys to peoples stashes of the virtual currency.

Yet some of these services have had just as much trouble securing their keys. Many of the largest Bitcoin exchanges over the years including the onetime well-known exchange Mt. Gox have lost private keys or had them stolen.

Gabriel Abed, 34, an entrepreneur from Barbados, lost around 800 Bitcoin now worth around $25 million when a colleague reformatted a laptop that contained the private keys to a Bitcoin wallet in 2011.

Mr. Abed said this did not dim his enthusiasm. Before Bitcoin, he said, he and his fellow islanders had not had access to affordable digital financial products like the credit cards and bank accounts that are easily available to Americans. In Barbados, even getting a PayPal account was almost impossible, he said. The open nature of Bitcoin, he said, gave him full access to the digital financial world for the first time.

The risk of being my own bank comes with the reward of being able to freely access my money and be a citizen of the world that is worth it, Mr. Abed said.

For Mr. Abed and Mr. Thomas, any losses from mishandling the private keys have partly been assuaged by the enormous gains they have made on the Bitcoin they managed to hold on to. The 800 Bitcoin Mr. Abed lost in 2011 were a small fraction of the tokens he has since bought and sold, allowing him to recently buy a 100-acre plot of oceanfront land in Barbados for over $25 million.

Mr. Thomas said he also managed to hold on to enough Bitcoin and remember the passwords to give him more riches than he knows what to do with. In 2012, he joined a cryptocurrency start-up, Ripple, that aimed to improve on Bitcoin. He was rewarded with Ripples own native currency, known as XRP, which rose in value.

(Ripple has recently run into legal troubles, in part because the founders had too much control over the creation and distribution of the XRP coins.)

As for his lost password and inaccessible Bitcoin, Mr. Thomas has put the IronKey in a secure facility he wont say where in case cryptographers come up with new ways of cracking complex passwords. Keeping it far away helps him try not to think about it, he said.

I got to a point where I said to myself, Let it be in the past, just for your own mental health, he said.

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Lost Passwords Lock Millionaires Out of Their Bitcoin Fortunes - The New York Times

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This cryptocurrency has more than doubled in 2021: Know all about it – CNBCTV18

After a strong 2020, the cryptocurrency market has started witnessing volatility in 2021 but the new year seems to have put the wind into the sails of at least one virtual currency.

Data from Coinmarketcap shows the price of Stellar (XLM) surged over 120 percent in the 12 days of 2021 to become the ninth-largest cryptocurrency by market capitalization.

On January 13, 3:45 pm, the token was trading at $0.29, up 123.07 percent, or $0.16, compared to the 2020 close of $0.13. Over one year, XLM is up 800 percent, with its latest spurt being accompanied by a rise in volumes -- dubbed by analysts as a good sign.

The immediate outlook for Stellar looks bullish, analysts said.

The price is above 50-day and 25-day exponential moving averages. Therefore, in my view, XLM will continue bouncing back as traders eye the all-time high of $0.40, Crispus Nyaga told InvestingCube. The cryptocurrency will have to fall drop below yesterdays low of $0.2123 to invalidate this trend, he added.

Analysts say this rally is driven by Ripple (XRPs) legal woes, such as its battle with the US Securities and Exchange Commission. (On December 22, SEC filed a lawsuit against Ripple Labs, XRPs largest stakeholder, for raising $1.3 billion over seven years by selling XRP to retail investors.)

Besides, XLM is also helped by Stellars announcement of its collaboration with Ukraine. The company is helping the Ukraine government in establishing a central bank digital currency (CBDC).

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What does the stars have in store for cryptocurrency? Astrologer tells all – IOL

By Reuters 9m ago

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By Anna Irrera and Tom Wilson

London - Bitcoin seems so flighty, some might argue you may as well consult a crystal ball, read the runes or stare at the stars to divine the direction of the capricious cryptocurrency.

Enter Maren Altman, bitcoin investor and astrologer.

The New Yorker has been following the movements of celestial objects to predict bitcoin price fluctuations since last summer. And while many people might mock her methods, she has built up a 1 million-strong social-media following on TikTok.

Last week, the 22-year-old told her followers to watch for a price correction on Jan. 11.

Why? Saturn was going to cross Mercury.

Lo and behold, bitcoin fell as much as 21% on that day, before recovering most of its losses, slamming the brakes on a meteoric rally that saw it double from early December to a record $42,000 last week.

"I am never going to tell someone to buy this or that," said Altman. "I can predict price trajectories but do not claim to be a financial adviser aware of someone's specific circumstances, and therefore never give buy or sell advice."

For the uninitiated, Mercury represents bitcoin's price data and Saturn is a restricting indicator.

While many people might give Altman's analysis as much credence as any fortune-telling, she is among a growing cohort of young TikTok influencers who began posting content on cryptocurrencies as prices rallied in 2020.

They're jumping on the bandwagon for bitcoin, whose mysterious movements even baffle many financial analysts, who say cryptocurrencies lack the fundamental data points used to assess traditional assets.

"I'm a bit of a cynic when it comes to bitcoin projections," said Craig Erlam, an analyst at forex broker OANDA. "I think it's just a selection of people clutching at straws, trying to justify any reasons to be bullish."

Bitcoin has jumped over five-fold since the start of 2020, prompting investment banks to predict more future gains. Citigroup said bitcoin could hit $318,000, while JPMorgan Chase & Co tipped it to reach $146,000.

So what do the stars have in store for the world's favourite cryptocurrency?

"I see some favourable indicators at the end of the month and especially February and early March," said Altman, whose readings of bitcoin's astrology charts are based on the date for the coin's genesis block, the equivalent of its birthday.

"However getting into mid-March, I see a big correction. Mid-April is also really less optimistic. May is bullish."

Reuters

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BCG GAMMA, in Collaboration with Scikit-Learn, Launches FACET, Its New Open-Source Library for Human-Explainable Artificial Intelligence – PRNewswire

BOSTON, Jan. 12, 2021 /PRNewswire/ --Boston Consulting Group (BCG)has released its first open-source software library for human-explainable artificial intelligence (AI). BCG GAMMA FACET enables users to make better business decisions by opening the "black box" of advanced machine learning models. Advances in AI have given data scientists powerful tools to analyze complex business problems and predict outcomes. FACET goes one step further, giving data scientists and business experts a new way to understand how a model arrives at predictions. With this new insight, data scientists can use machine learning models to inform decisions that save money, maximize yield, retain customers, remove bias, and improve patient outcomes.

BCG believes that humans must always be at the core of all AI-based decisions. By helping developers and business users understand how algorithms analyze the data sets on which AI predictions are based, BCG GAMMA FACET reestablishes human control over and trust in AI. It uses a newly developed model-inspection algorithm to explain the relationships between the model variables. And it applies a simulation approach to enable data scientists to conduct "virtual experiments" to determine how changes in these variables can affect predicted outcomes.

"Data scientists are often under pressure to explain the behavior of their models. This is precisely the aim of FACET: to explain the key variables in the models very quickly, in order to provide greater clarity in the dialogue between data scientists and operational teams. By facilitating the explicability of models, FACET contributes to the deployment of a more transparent and more responsible AI," says Sylvain Duranton, BCG managing director, senior partner, and global leader of BCG GAMMA.

"We are very glad that scikit-learn's simple and consistent design allowed BCG to develop FACET, a very valuable tool for our community," says Alexandre Gramfort, senior research scientist at Inria, co-author, and member of the scikit-learn technical committee.

"BCG GAMMA is very excited to join the open-source data science community with our public release of FACET," says Jan Ittner, BCG partner, associate director, and leader of the BCG GAMMA FACET team. "We look forward to working with the data science community and in partnership with scikit-learn to make AI more useful and understandable for everyone."

BCG GAMMA FACET is an intuitive, easy-to-implement, open-source software library available to the global data science community.

For more information about BCG GAMMA FACET, please contact Sophie Ruedinger at +49 170 334 4530 or [emailprotected].

For more media queries, please contact Eric Gregoire at +1 617 850 3783 or [emailprotected].

ABOUT BCG GAMMABCG GAMMA is BCG's global team dedicated to applying artificial intelligence and advanced analytics to critical business problems at leading companies and organizations. The team includes 900-plus data scientists and engineers who utilize AI and advanced analytics (e.g., machine learning, deep learning, optimization, simulation, natural language and image analytics, etc.) to build solutions that transform business performance. BCG GAMMA's approach builds value and competitive advantage at the intersection of data science, technology, people, business processes, and ways of working. For more information, please visit our webpage.

About Boston Consulting GroupBoston Consulting Group partners with leaders in business and society to tackle their most important challenges and capture their greatest opportunities. BCG was the pioneer in business strategy when it was founded in 1963. Today, we help clients with total transformationinspiring complex change, enabling organizations to grow, building competitive advantage, and driving bottom-line impact.

To succeed, organizations must blend digital and human capabilities. Our diverse, global teams bring deep industry and functional expertise and a range of perspectives to spark change. BCG delivers solutions through leading-edge management consulting along with technology and design, corporate and digital venturesand business purpose. We work in a uniquely collaborative model across the firm and throughout all levels of the client organization, generating results that allow our clients to thrive.

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https://www.bcg.com

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How Professors Can Use AI to Improve Their Teaching In Real Time – EdSurge

The original version of this article appeared in Toward Data Science.

When I started teaching data science and artificial intelligence in Duke Universitys Pratt School of Engineering, I was frustrated by how little insight I actually felt I had into how effective my teaching was, until the end-of-semester final exam grades and student assessments came in.

Being new to teaching, I spent time reading up on pedagogical best practices and how methods like mastery learning and one-on-one personalized guidance could drastically improve student outcomes. Yet even with my relatively small class sizes I did not feel I had enough insight into each individual students learning to provide useful personalized guidance to them. In the middle of the semester, if you had asked me to tell you exactly what a specific student had mastered from the class to date and where he or she was struggling, I would not have been able to give you a very good answer. When students came to me for one-on-one coaching, I had to ask them where they needed help and hope that they were self-aware enough to know.

Knowing that my colleagues in other programs and universities teach much larger class sizes than mine, I asked them how aware they felt they were of each of their students level of mastery at any point in time. For the most part, they admitted they were also largely flying blind until final assessment results came in. It is historically one of the most vexing problems in education that there is a tradeoff between scale and achievable quality of instruction: As class sizes grow larger, the ability of a teacher to provide the type of personalized guidance shown by learning science research to be most effective is diminished.

Yet as instructors in the new world of online education, we have access to ever-increasing amounts of datafrom recorded lecture videos, electronically submitted homework, discussion forums, and online quizzes and assessmentsthat may give us insights into individual student learning. In summer 2020, we began a research project at Duke to explore how we could use this data to help us as instructors do our job better. The specific question we set out to answer was: As an instructor, how can I use the data available to me to support my ability to provide effective personalized guidance to my students?

What we wanted to know was, for any given student in a class at any point during a semester, what material have they mastered and what are they struggling with? The model of Knowledge Space Theory, introduced by Doignon and Falmagne in 1985 and significantly expanded on since, posits that a given domain of knowledge (such as the subject of a course) contains a discrete set of topics (or items) that often have interdependencies. The set of topics that a student has mastered to date is called their knowledge state. In order to provide effective instruction for the whole class and to provide personalized guidance for individual students, understanding the knowledge state of each student at any point is critical.

So how does one identify a students knowledge state? The most common method is through assessmenteither via homework or quizzes and tests. For my classes, I use low-stakes formative quiz assessments each week. Each quiz contains around 10 questions, with roughly half of the questions evaluating student knowledge of topics covered in last weeks lecture, and the remaining half covering topics from earlier in the course. In this way, I continue to evaluate students mastery of topics from the whole course each week. In addition we have weekly homework, which tests a variety of topics covered to date.

But digging through dozens or hundreds of quiz or homework question results for tens or hundreds of students in a class to identify patterns that provide insight on the students knowledge states is not the easiest task. Effective teachers need to be good at a lot of thingsdelivering compelling lectures, creating and grading homework and assessments, etc.but most teachers are not also trained data scientists, nor should they have to be to do their jobs.

This is where machine learning comes in. Fundamentally, machine learning is used to recognize patterns in data, and in this case the technology can be used to identify students knowledge states from their performance patterns across quizzes and homework.

To help improve my own teaching and that of my fellow faculty members in Dukes AI for Product Innovation masters program, we set out to develop a system that could, given a set of class quiz and homework results and a set of learning topics, identify each students learning state at any time and present that information to both instructor and learner. This would facilitate more effective personalized guidance by the instructor and better awareness on the part of the student as to where they need to put additional focus in their study. Additionally, by aggregating this information across the class, an instructor could gain insight into where the class was successfully learning the material and where he or she needs to reinforce certain topics.

The project culminated in the creation of a prototype tool called the Intelligent Classroom Assistant. The tool reads instructor-provided class quiz or homework results and the set of learning topics covered so far in the course. It then analyzes the data using a machine learning algorithm and provides the instructor with three automated analyses about: quiz and homework topics with which the class has struggled; learning topics the class has and has not mastered; and the performance of each student.

One of the key challenges in developing the tool was the mapping of quiz and homework questions to the most relevant learning topic. To accomplish this, I developed a custom algorithm that uses natural-language processing and draws on open-source libraries to understand the context of each question and map it to the primary learning topic it was designed to evaluate.

The Intelligent Classroom Assistant tool was built while I taught the Sourcing Data for Analytics course at Duke, an introductory-level data science course for graduate engineering students that covered technical as well as regulatory and ethical topics. This gave me an opportunity to try out the tool on my class as the semester progressed.

One of the key things I wanted to evaluate was how well the algorithm behind the hood of the tool could classify each quiz or homework question into the most relevant of the 20 learning topics covered in the course. On the full set of 85 quiz questions I used during the semester, the algorithm identified the relevant learning topic correctly about 82 percent of the time. While not perfect, this was good enough to make the analyses provided by the tool useful to me.

During the course, I used the prototype in two main ways to inform my teaching. I spent extra time in lecture sessions covering learning topics and specific quiz questions that the tool flagged due to low student performance. And during one-on-one help sessions with students, I used the personalized student analysis module of the tool to understand where the student needed extra reinforcement and make tutoring sessions more focused.

It's too soon to quantify whether the tool changed student outcomes, because the course I used it in was new, which means there is no historical benchmark for comparison. But this year, we are expanding the tool's use and are working to evaluate the effects it has on student engagement and performance. We are trying it out in another engineering class of 25 and also in an undergraduate finance class of more than 200 students. I also plan to use the prototype in my spring machine learning class to guide my teaching through the semester. Since students can benefit from seeing the results of the tools analysis as much as instructors, for spring we hope to include the addition of a student portal allowing students to see their own results and providing personalized study recommendations to students based on their identified knowledge state.

The amount of electronic data now available to instructors can help support their teaching. But teachers are not (usually) data scientists themselves, and need analytics tools to help them extract value from the data. While such tools are helpful, however, their value is directly proportional to how well an instructor defines course learning objectives and structures material and assessments to support and evaluate those objectives.

Machine learning tools such as The Intelligent Classroom Assistant can not only help teachers to improve the quality of their classes (as measured by student learning outcomes), but also enable them to do so at increased scale, offering the promise of widespread personalized teaching. When teachers can teach more effectively, learners can learn more, and as a society we all reap the benefits.

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Calling all rock stars: hire the right data scientist talent for your business – IDG Connect

This is a contributed article by Scott Zoldi, Chief Analytics Officer, FICO.

Try googling rock star and data scientist, and prepare to be amused. Its actually a thingusing rock star and data scientist in the same sentence. Dont get me wrong, I get it. As a data scientist myself, working with some of the most brilliant minds in the industry, Im amazed by the creativity, intelligence, vision and raw talent of my colleagues. They collaborate every day, harmonising their strengths and expertise around responsible AI to solve the big issues facing our business, our industry and our world. Theyre working to correct financial inequity and disparity. Theyre developing machine learning to stop financial crime and money laundering. Theyre developing tools and platforms for others to leverage at scale. Their set list is long, and Im proud to be their biggest fan and collaborator.

Executives sometimes say to me, You make AI sound easy. How can my company get started? First, its not easy, often complicated by the team structure and organisational philosophies at play. Second, you start by building a rock star analytics team a carefully selected ensemble that balances each data scientists strengths, while also recognising and addressing capability gaps on the overall team.

Its an up-front investment that wont come cheap. Demand for data science talent is high. However, demand for AI products is also up since the onset of COVID-19, according to a recent Corinium survey. If youre thinking of building your own group of analytic artists, here are a few guidelines to consider.

Before assembling a team that makes beautiful music together, you first need to stop, take a hard look at your organisation and ask questions. What are you trying to accomplish with this team? What resources do you already have in place technology, expertise and executive sponsorship to support this team? What are your companys data analytic strengths and weaknesses, and how can this team impact those areas? How will this team engage and communicate with others within the organisation and deliver value to the business? Will this team engage externally, with customers and industry peers? What is your budget? How will you measure the ROI of the team?

Theres no template or magic formula for getting it right. In fact, 65 percent of AI leaders admit that building a team with the right skills is a significant barrier to AI adoption, according to a July 2020 Corinium report. Furthermore, its worth exploring how to incorporate greater gender and ethnic diversity as you set out to build your analytics dream team. According to a McKinsey report, companies and teams with greater gender, ethnic and cultural diversity outperform industry peers by up 33 percent.

Its an iterative process where you ask the hard questions early and often to produce a successful outcome. First and foremost, the team should appropriately balance the companys current level of analytics sophistication and aspirations for AI adoption. From there, you can determine the right size and capabilities of the team based on organisation-specific needs and objectives.

Once you set the stage, then you can focus on talent. The key here is diversity look for a mix of skills sets and talents. Think of it this way: you only need one Elvis. In turn, he needs a band of great musicians to be successful. Indulge me as I run with this analogy and share my thoughts on key positions that comprise a rock star analytics team.

In my (admittedly biased) opinion, todays data scientists have earned their rock star status. Theyre transforming our world with AI-driven processes that fuel next-level performance and better business outcomes. But, before jumping on the bandwagon, take the time to consider whats right for your organisation. To build a balanced, functional team that fits the needs of your organisation, be selective when choosing your team and take the time to understand the unique role each scientist plays in the band.

Scott Zoldiis Chief Analytics Officer atFICO,driving the company's innovation in artificial intelligence and incorporating it into FICO solutions.While at FICO,Zoldihas been responsible for authoring 110 analytic patents with 56 patents granted and 54 in process. He is an industry leader in developing practical applications and standards for AI, Explainable AI, Ethical AI and Responsible AI, and was named one of Corinium's 2020GlobalTop 100 Innovators in Data & Analytics.

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Companies hiring data scientists in NYC and how much they pay – Business Insider

Data science is a fast-growing field. According to the Bureau of Labor Statistics, it's growing much faster than the average occupation and is expected to continue to grow through 2029. In 2018, LinkedIn found that there was a shortage of over 150,000 people in the data science field.

Insider examined data from the US Office of Foreign Labor Certification, LinkedIn, and company job sites to understand salaries in the data science world, and who is currently hiring.

When a US company wants to hire someone from another country, the company has to file for a visa through the US Office of Foreign Labor Certification. The visa applications, which are published online, include salary data that is helpful in understanding how specific firms compensate employees.

The most common visa is called an H-1(B) visa, which is used for most professional positions. It's worth noting that the information only represents salary, and excludes other types of compensation like bonuses and benefits that most employees receive.We looked at visa applications from 2018 to 2020 for each firm.

Let's dig into what Google, Facebook, Spotify, Aetna, and more pay data scientists in New York City, and the positions that are open as of January 4.

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Which Technology Jobs Will Require AI and Machine Learning Skills? – Dice Insights

Artificial intelligence (A.I.) and machine learning seem poised to dominate the future. Companies everywhere are pouring resources into making their apps and services smarter. But which technology jobs will actually require A.I. skills?

For an answer to that question, we turn to Burning Glass, which collects and analyzes millions of job postings from across the country. Specifically, we wanted to see which professions had the highest percentage of job postings requesting A.I. skills. Heres that breakdown; as the clich goes, some of these results may surprise you:

What can we conclude from this breakdown? Although you might think that artificial intelligence skills are very much in demand among software developers and engineers (after all, someone needs to build a smarter chatbot), data science is clearly the profession where A.I. is most in vogue.

Indeed,theres a lot of overlap between A.I. and data science. Both disciplines involve collecting, wrangling, cleaning, and analyzing massive amounts of data. But whereas a data scientist will analyze data for insights that they present to the broader organization, artificial intelligence and machine learning experts will use those datasets to train A.I. platforms to become smarter. Once sufficiently trained, those platforms can then make their own (hopefully correct) inferences about data.

Given that intersection of artificial intelligence and data science, many machine-learning and A.I. experts become data scientists, and vice versa. That relationship will likely only deepen in the years ahead. Burning Glass suggests that machine learning is a defining skill among data scientists, necessary for day-to-day tasks; if youre aiming for a job as a data scientist, having extensive knowledge of artificial intelligence and machine-learning tools and platforms can give you a crucial advantage in a crowded market.

Many other technologist roles will see the need for artificial intelligence skills increase in the years ahead. If youre involved in software development, for instance, learning A.I. skills now will prepare you for a future in which A.I. tools and platforms are a prevalent element in many companies tech stacks. And make no mistake about it: Managers and executives will alsoneed to become familiar with A.I. concepts and skills.A.I. is not going to replace managers but managers that use A.I. will replace those that do not, Rob Thomas, senior vice president of IBMscloudand data platform,recently told CNBC.

Overall, jobs utilizing artificial intelligence skills are projected to grow 43.4 percent over the next decade; the current median salary for jobs that heavily utilize A.I. skills is $105,000, higher than for many other professions. It must be noted, though, that A.I. and machine learning are areas where you really need to know your stuff, and hiring managers will surely test you on both your knowledge of fundamental concepts as well as your ability to execute. When applying for A.I.-related jobs, a portfolio of previous projects can only help your prospects.

Granted, its still early days for A.I.: Despite all the hype, relatively few companies have integrated A.I. into either their front-end products or back-end infrastructure. Nonetheless, its clear that employers are already interested in technologists who are familiar with the A.I. and machine learning platforms that will help determine the future.

Want more great insights?Create a Dice profile today to receive the weekly Dice Advisor newsletter, packed with everything you need to boost your career in tech. Register now

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This Is the Best Place to Buy Groceries, New Data Finds | Eat This Not That – Eat This, Not That

If you asked someone what their favorite place to get groceries is, would you be surprised if they said Amazon? A new report reveals that it's the top place to shop for groceries, based on consumers' preferences.

According to dunnhumby, the global leader in consumer data science, Amazon reigned supreme in the platform's fourth annual Retailer Preference Index (RPI)an exhaustive, nationwide study of best places to buy groceries, which takes consumers' emotional connection to grocery chains as well as prices into consideration. (Related: 8 Grocery Items That May Soon Be in Short Supply.)

In the report, dunnhumby surveyed roughly 10,000 U.S. households about their preferred grocers, and of the 56 retailers included, Amazon dominated the scene. It received the highest overall customer preference score in the first quartile. Behind Amazon is the former winner, H-E-B, followed by Trader Joe's, Wegman's, and Aldi's.

Last year, Amazon placed third in the overall RPI but has now jumped to first place, primarily because of how well-positioned it was to deliver on many of the key areas measured in the index such as speed (second place) and digital (first place). The online retailer took 11th place in the price category, ensuring a strong product value.

The report evaluated seven drivers of customer preference, including price, quality, digital, operations, convenience, discounts, rewards & information, and speed. In addition, it assessed how likely a retailer's customer value proposition will set the company up for long-term financial success.

As of this year, the data science platform also factored in a Covid Momentum Metric, which explores the short-term financial successes each retailer experienced during the pandemic, and Amazon (to no surprise) claimed the first spot in that category, as well.

"Amazon accelerated past every other retailer on our Covid Momentum Metric and customer safety ratings, due to its speed to shop and virtual store format. As we begin to emerge from the pandemic, we should expect value perception to come back strongly. Beyond Covid, retailers with Customer First strategies will best adapt to changing behaviors and deliver what matters most to their customers," said Grant Steadman, President of North America for dunnhumby in a statement.

For more, be sure to check out6 Grocery Stores That Are Already Offering the COVID-19 Vaccine.

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Rapid Insight To Host Free Webinar, Building on Data: From Raw Piles to Data Science – PR Web

Free Webinar: January 12th, 2 PM ET | 11 AM PT

CONWAY, N.H. (PRWEB) January 11, 2021

Organizations frequently hire data practitioners to build a new data infrastructure from the ground up, but many lack a formal data strategy or implementation plan. On Tuesday, January 12th at 2 PM ET (11 AM PT), analytics software provider Rapid Insight will host a free webinar offering foundational advice for structuring an organizations data.

The webinar will feature Caitlin Hudon, Lead Data Scientist at OnlineMedEd. Hudon will walk through establishing a new data infrastructure at an organization, assembling the tools and processes required to succeed, and building a culture of data for continued success. The webinar will also feature Jon MacMillan, Product Manager at Rapid Insight.

In addition to an overview of establishing an effective greenfield data program, Hudon will offer advice on creating immediate value with data while laying a foundation for long-term success. She will discuss establishing a communications plan and intake system for data requests and developing a data dictionary for team members and end-users.

For analysts tasked with setting their organization up for success with data, this webinar will be a valuable resource, said Mike Laracy, President and Founder of Rapid Insight. Hudons experience and wisdom will help you assemble an actionable plan, and Rapid Insights intuitive tools and expert analyst support can help you execute it.

This webinar explores a highly-relevant topic. Organizations around the world are recognizing the value of structured data and hiring analysts to operationalize it. This webinar will equip those assigned to organize and analyze unstructured data with a clear path toward building data science.

To learn more about this webinar and to register, click here.

About Rapid Insight:Rapid Insight is a leading provider of business analysis and automated predictive analytics software. With a specialty in higher education and a focus on ease of use and efficiency, Rapid Insight products enable users to turn their raw data into actionable information. The companys analytic software simplifies the extraction and cleansing of data, equipping organizations of all sizes with data-informed decision making. For more information, visit http://www.rapidinsight.com.

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Rapid Insight To Host Free Webinar, Building on Data: From Raw Piles to Data Science - PR Web

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