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Cloud providers team with telcos as 5G offers new growth – S&P Global

August 14, 2021 | Author: admin

Cloud-computing giants and phone companies have decided they need each other to drive the market for 5G business services.

Amazon Web Services, Google Cloud and Microsoft Corp. have all signed partnerships with telcos in recent months as they seek new opportunities amid slowing growth in demand for data storage. Telcos, including AT&T Inc. and Verizon Communications Inc., are also looking to revive flagging revenue while tapping into cloud computing's ability to make super-fast 5G networks more efficient and cheaper to run.

"The cloud companies need to reinvent themselves," said Omdia analyst Vlad Galabov. "Communications service providers have the same thing everyone has a mobile phone, a network so both have saturation."

The parties are working together to bring computing functions such as data processing onto the 5G network, closer to where data is generated. This system, known as multi-access edge computing, or MEC, enables partners to offer enterprise services such as real-time analytics, manufacturing automation and live product tracking.

Verizon and Amazon Web Services the cloud arm of Amazon.com Inc. are offering this feature to individual businesses after expanding their partnership to include Private MEC in April.

Verizon also has a separate tie-up with International Business Machines Corp. to jointly develop MEC services.

"Demand for the technology is global, which gives both companies a lot of potential for new opportunities and markets," IBM Vice President Marisa Viveros told S&P Global Market Intelligence. IBM boosted its second-quarter cloud revenue 13% to $7 billion this year.

Google Cloud has signed deals with the likes of AT&T, Telefnica SA and Telecom Italia SpA. The Alphabet Inc. unit wants to help telcos monetize 5G as a "business services platform," Google Cloud CEO Thomas Kurian said in 2020.

Microsoft and AT&T took their partnership to a new level in June, with the software company agreeing to buy 5G technology and engineering staff from the telco. Microsoft will now manage AT&T's 5G network traffic, and the telco will continue to operate the infrastructure.

"In the future, cloud providers could take on more network-based workload opportunities, and thus potentially more operational roles," an AT&T spokesperson told S&P Global Market Intelligence.

Such a trend could ultimately raise the question of who actually operates 5G infrastructure telcos or cloud companies said Kevin Restivo, research manager of European Enterprise Mobility at IDC. "We're just seeing the groundwork being laid for the next decade of how telcos are run and who wins and who loses out of that."

It may also make it harder for telcos to differentiate themselves if they are all relying on the same cloud-computing providers, Omdia's Galabov said.

Verizon is not contemplating farming out its network operations, as the telco "sees incredible value in owning its own network," a company spokesperson said. Telefonica and Telecom Italia did not respond to requests for comment.

For now, telcos are focused on drawing on cloud-computing providers' strengths to help hasten the rollout of enterprise 5G offerings.

Cloud-computing giants "can help the telcos monetize investment by adding value and offering a channel to the customers," said Brian Partridge, an analyst at 451 Research, a research division of S&P Global Market Intelligence. They are "framing what they do well and overlapping that with what the operators do well."

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Cloud providers team with telcos as 5G offers new growth - S&P Global

Jordan Peterson wraps his divisive giga-brain around Bitcoin – Cointelegraph

August 14, 2021 | Author: admin

Controversial Canadian psychologist Jordan Peterson appears to have turned onto Bitcoin (BTC) in the latest episode of his podcast.

On Tuesday, Peterson published a podcast titled Bitcoin: The Future of Money? which hosted a panel of Bitcoiners including John Vallis, the host of the Bitcoin Rapid-Fire podcast; Bitcoin coder Der Gigi; film creator Richard James; and Robert Breedlove, ex-hedge fund manager and host of the What is Money? show.

In the video, Peterson, who claims to have an IQ of around 150, puts forward a succinct description of the innovation from which Bitcoin derives its value:

Throughout the episode, the 59-year-old author prompted his guests to provide their views on the value that Bitcoin provides to society, and in turn, he then rearticulated their answers back to them in an attempt to form a fundamental understanding of its key concepts.

So, [Bitcoin] is completely transparent. Its completely distributed. Theres no centralized authority. It cant be cracked. It cant be stolen. It doesnt inflate. It cant be inflated. It isnt subject to any form of overt administrative control, he said.

While Peterson isnt known as a crypto proponent, he may know more about Bitcoin and blockchain technology than he let on in the video. The psychologist started accepting BTC donations back in 2018 after he boycotted Patreon over free speech issues.

Peterson has publicly discussed the significance of blockchain tech on multiple occasions, and during an interview with Grant Blaisdell in January 2020, he tentatively stated that:

Peterson also questioned the guests on what they thought were the downsides of Bitcoin and referred to Elon Musks environmental concerns surrounding the sustainability of mining practices behind the asset.

Related:Is being late into Bitcoin about perspective?

The consensus among the guests was that the energy required to maintain the Bitcoin network was worth it because of its transformative effects on society in terms of decentralization, with Gigi suggesting that society, in general, asks these questions about all kinds of things, Are cars worth it? Are smartphones worth it? Is the internet worth it?

Peterson then boiled down the discussion by stating if Bitcoins value propositions were found to be true, the result would be that:

And so therell be a net energy gain not a net energy loss if you calculated it across the entire system. And so, its a mistake just to look at the cost of generating Bitcoin in the absence of considering the efficiencies that Bitcoin would produce, he said.

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Jordan Peterson wraps his divisive giga-brain around Bitcoin - Cointelegraph

Jordan Peterson Learns About Bitcoin And The Whole World Benefits. Part 1/ 2 – bitcoinist.com

August 14, 2021 | Author: admin

Its amazing how fast Jordan Peterson caught on to the complex ideas behind Bitcoin. The Canadian psychologist and media personality invited four very different Bitcoin experts to his podcast. Their conversation is deep but easy to follow, a treasure for future educators. Its not exactly a Bitcoin 101 class, Peterson is so sharp that he easily grabs the core concepts and, armed with that, asks the right questions. He also synthesizes what his interviewees tell him in one-liners that we can all use from here on out.

Related Reading | China Gives Out $40 Million Of Digital Yuan In Red Envelopes To Boost Adoption

This podcast episode is a mandatory watch for everyone interested in Bitcoin, and we transcribed the key quotes and ideas for you all to use and spread around. Fasten your seatbelts, were going far, above, and beyond.

The podcast title is: Bitcoin: The Future of Money? and it came to be because the Bitcoiner Book Club read Maps of Meaning by Jordan Peterson. He invited:

This gang of misfits doesnt waste one second. Valis defines Bitcoin as an extreme form of ownweship that wasnt available in the world until Satoshi Nakamoto conjured it. He claims that Bitcoin changes your relationship to responsibility and lowers your time preference. Bitcoin fixes the incongruencies that a system with fake fiat money generates. This idea will be the episodes leitmotiv.

For more information on the time preference concept read our analysis of The Bitcoin Standard.

From everything the guest says, Jordan Peterson deduces that Bitcoin provides an incorruptible language of value. James defines Austrian economics as a system that uses logical deduction rather than empiricism. Jordan Peterson was reading on the subject and defines economics as The science of comparative value. And then, money becomes an index of that comparative value.

Its time for Breedlove to answer the question that serves as his podcasts name, what is money? Money is a contract of the future. And today, fiat currency is a violated social contract. He claims that inflation effectively robs out future and corrodes socioeconomic fabric, social morality. And, to drive the point home, artificial central-bank-induced inflation is a corrosive moral cancer on society.

For more information on government money and hyperinflation, read our analysis of The Bitcoin Standard.

Its time for the programmer to participate, Gigi defines Bitcoin as a distributed system of accounting that checks and verifies copies of this ledger and makes invalid copies useless. On the users side, ideally, only you know and own your private keys. Cryptography is inherently defensive and those keys are the secret that unlocks your funds. If you know it, theres no problem. If you dont, there are infinite possibilities to try. The whole system is probabilistic. Chances are no one will guess your private keys. Ever.

An ambitious Jordan Peterson asks Gigi to simply define Bitcoin. He tries, its a list of transactions that is transparent and radically distributed. Of course, thats just one aspect of it.

The Dollar is a pyramid scheme, claims Breedlove. The free market itself, as youve described it, Jordan, is a distributed computing system. Any intervention, any regulation, is a move towards an economic tyrany, because it pushes the desires of a few.

For more information on the magical properties of prices to describe our reality read our analysis of The Bitcoin Standard.

According to Valis, inflation dilutes money artificially and that introduces incongruencies in the system. If we had pristine information, that would inevitably cause emerging order. Then, he uses Jordan Peterson s vocabulary to drive the point home. Inflation is changing the relationship between the matrix of value hierarchies without decremental sacrifices, and thats what creates pathological hierarchies.

Here, Jordan Peterson has a breakthrough. So, you guys really do see it as a distributed form of governance. Boom! Thats what Bitcoin is. Of course, thats just one aspect of it.

Related Reading | $3.6 Billion Crypto Theft: South African Bank Denies Relationship With Fraud Accused Africrypt

Risk is error, Breedlove claims to close this point. Because we have centrally planned money, its pushing hidden risk into the economy. Those false signals confuse the free market distributed intelligence. Intelligence, defined as error correction, is mitigated by the central planning of money.

And then, Jordan Peterson synthesizes the whole point. So, you think of incorruptible money as computationally advantageous, essentially.

Impressive. The man really got it.

In part 2, the gang tackles Elon Musks environmental claims, when will Bitcoin become a medium of exchange, and the risks it brings into the world.

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Jordan Peterson Learns About Bitcoin And The Whole World Benefits. Part 1/ 2 - bitcoinist.com

Steven Weinberg and the twilight of the godless universe – The Jerusalem Post

August 14, 2021 | Author: admin

With the passing last month of Steven Weinberg, the world lost a great theoretical physicist. Born to Jewish parents in New York in 1933, Weinberg received the Nobel Prize in 1979 for unifying two of the four fundamental forces of physics, the electromagnetic and weak nuclear forces. His proposed unification, later confirmed by experiment, proved key to the development of the Standard Model of particle physics, the best current theory of fundamental physics and our guide to the strange world of elementary particles. In addition, Weinberg made seminal contributions to quantum theory, general relativity and cosmology.

His death also marks the twilight of an increasingly dated view of the relationship between science and religion. Though Weinberg was a friend to the State of Israel, he was not sympathetic to Judaism or any theistic belief. Weinberg wrote many popular books about physics in which he often asserted that scientific advance had undermined belief in God and, consequently, any ultimate meaning for human existence. The First Three Minutes, his most popular book published in 1977, famously concluded: the more the universe seems comprehensible, the more it seems pointless.

Weinbergs aggressive science-based atheism now seems an increasingly spent force. Since 1977, Carl Sagan, Richard Dawkins, Stephen Hawking, Victor Stenger, Lawrence Krauss and many other scientists have published popular anti-theistic broadsides. Many of these stalwarts have since passed from the scene. Others have so overplayed their hands with overt attacks on religion that they have provoked even fellow atheists and agnostics to recoil.

Figures such as historian Tom Holland, social critic Douglas Murray, psychologist Jordan Peterson and social scientist Charles Murray now openly lament the loss of a religious mooring in culture, though they personally find themselves unable to believe. These New New Atheists, as distinct from the Old New Atheists, do not regard sciences alleged support for unbelief as one of its great achievements, as Weinberg described it.

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Nevertheless, many such religious skeptics have yet to recognize the most important reason to reject science-based atheistic polemics: The most relevant scientific discoveries of the last century simply do not support atheism or materialism. Instead, they point in a decidedly different direction.

In The First Three Minutes, Weinberg described in detail the conditions of the universe just after the Big Bang. But he never attempted to explain what caused the Big Bang itself.

Nor could he. If the physical universe of matter, energy, space and time had a beginning as observational astronomy and theoretical physics have increasing suggested it becomes extremely difficult to conceive of an adequate physical or materialistic cause for the origin of the universe. After all, it was matter and energy that first came into existence at the Big Bang. Before that, no matter or energy no physics would have yet existed that could have caused the universe to begin.

Such considerations have led other prominent scientists such as Israeli physicist Gerald Schroeder and the late Caltech astronomer Allan Sandage to affirm an external creator beyond space and time as the best explanation for the origin of the universe. The logic of this view made Weinberg initially reluctant to accept the Big Bang and inclined him, instead, to favor the rival steady state theory. As he explained before coming around, the steady state is philosophically the most attractive theory because it least resembles the account given in Genesis.

Fellow Nobel laureate and physicist Arno Penzias whose discovery of the cosmic background radiation helped kindle Weinbergs interest in Big Bang cosmology noted the obvious connection between the Big Bang and the concept of divine creation. As he argued, the best data we have are exactly what I would have predicted had I nothing to go on but the first five books of Moses, the Psalms and the Bible as a whole.

Weinberg also brilliantly used anthropic reasoning to estimate the value of the cosmological constant the outward pushing, anti-gravity force responsible for the expansion of the universe from its singular beginning. He showed that if we assume the universe needed to produce life, then the cosmological constant had to fall within a narrow, highly improbable and otherwise unexpected range as has proven to be the case.

To explain such extreme fine tuning without recourse to a transcendent fine-tuner, Weinberg favored the postulation of a multiplicity of other universes, an idea he acknowledged as speculative. The multiverse concept portrays our universe as the outcome of a grand lottery in which some universe-generating mechanism spits out trillions and trillions of universes so many that our universe with its improbable combination of life-conducive factors would eventually have to arise.

Yet, multiverse advocates overlook an obvious problem. All such proposals posit universe generating mechanisms that themselves require prior unexplained fine-tuning thus, taking us back to the need for an ultimate fine-tuner.

On his passing, Scientific Americans tribute to Weinberg described how scientifically literate people need to learn to live in Steven Weinbergs pointless universe. Yet Weinbergs own research built upon, or helped to make, two key scientific discoveries the universe had a beginning and has been finely-tuned from the beginning that do not imply a purposeless cosmos. Arguably, they point, instead, to a purposeful creator behind it all.

The writer is director of Discovery Institutes Center for Science & Culture and the author most recently of Return of the God Hypothesis: Three Scientific Discoveries That Reveal the Mind Behind the Universe.

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Steven Weinberg and the twilight of the godless universe - The Jerusalem Post

What Deepfakes Can Do: The Best Videos And the Deepfake Apps Changing Reality – TechTheLead

August 14, 2021 | Author: admin

For the last four years, deepfake videos have challenged reality, turning empirical evidence into an artifact of the past. Can you really trust what you see online?

The technology soon spread out on sites like Reddit, with mainstream media featuring headlines ever more alarming we chronicled the major happenings here.

No matter what you feel about deepfakes, this tech is here to stay and these are the best deepfake videos you should know about. From the funny to the alarming or downright unsettling, we compiled the best deepfake videos through the years and chronicled the rise of this technology.

Deepfakes take their name from the deep learning AI technique and the word fake, so this term means fake images created through deep learning.

Of course, photo manipulation is nothing new, but the academics behind deepfakes brought the concept to videos and changed the world as we know it.

An AI technique that combines existing images and videos to create new images and videos, usually clones indistinguishable from reality, deepfakes came to prominence in 2017 with a viral video of former US President Obama.

As we said in our previous report on this tech, the main techniques used to make deepfakes are based on deep learning, training generative neural network architectures or using generative adversarial networks (GANs).

The first notorious example, the original Obama deepfake, demonstrated a very advanced lip syncing technology where the subjects say whatever the computer tells them to say, without needing to be in the studio and record the voice.

This viral video came after other computer scientists played with images of Donald Trump and Vladimir Putin but, back then, the tech was still considered innocent.

In that project, the researchers envisioned their tech being used for better foreign language dubbing in movies and even accurate live video chat translations.

Fast forward a couple of years and the US president is saying whatever is fed into the program, we have deepfake pornography, deepfake voice scams and deepfake apps anyone could use to become their favorite actor. In a very short time, this tech took over the internet.

Indeed, you can make your own deepfakes with just a couple of clicks or taps.

The Zao deepfake app lets you upload an image of your face and turns you into your favorite actor or superhero. While the app was branded more like a face-swap app, the accuracy with which Zao superimposes faces on pre-selected videos is more akin to deepfake technology.

It was the start of the face-swap apps madness and even social media giants jumped on the trend.

First, there were the filter makers of Instagram, who offered premium AR filters to add regular peoples faces into existing videos.

Then, the Snapchat Cameo feature, the first official one from a major social media platform, used deepfake tech to let users add their own faces to different activities and turn the result into a shareable gif.

While these apps were used to create cute, shareable content, the deepfake tech does have a dark side to it.

Privacy experts around the world sounded the alarm on deepfakes and how they could be used to generate fake news. Even actor Jordan Peele took over Barack Obamas face in a deepfake warning about how this tech could impact the social and political landscape.

However, according to one report, this tech could have a more immediate negative impact on regular Internet users.

According to the The State of Deepfakes study authored by cybersecurity company Deeptrace, the majority of deepfakes floating online are porn deepfakes, and not videos used to support fake news. And the numbers paint a very sketchy picture.

The researchers found a total 14,678 deepfake pornographic videos online. Alarmingly, 96% of them were non-consensual.

Deepfake pornography accounts for a significant majority of deepfake videos online, even as other forms of non-pornographic deepfakes have gained popularity, warned the experts, and the broken down numbers look no better.

Deepfake pornography is a phenomenon that exclusively targets and harms women, said researchers, who found the content on deepfake pornography websites to have an 100% female content, unlike on YouTube, where women were deepfaked in just 39% of the videos.

According to their report, all but 1% of the subjects featured in deepfake pornography videos were actresses and musicians working in the entertainment sector.

The authors chose to not publish the names of women targeted by deepfakes but they did provide a breakdown of the most popular categories.

While the entire world was focused on the dangers of deepfakes on the political scene and the effects of disinformation, this tech was quietly spreading as a destructive social force at the most intimate level.

A report called The Double Exploitation of Deepfake Porn focused on revenge porn and IP theft as the more pressing concerns.

First, unsuspecting women are fetishized by the making of a deepfake video, humiliated or subjected to blackmail. Then, the videos used to create those deepfakes are actually the livelihood of sex workers online, who make a living from their original content.

In some cases, deepfake technology has been used as a tool to emotionally manipulate and shame.

In one report, a mother used explicit deepfake photos and videos to kick her daughters cheerleading rivals off the team. She combed through her daughters social media friends, took their pictures, and then used one of the popular deepfake apps to portray daughters colleagues naked, drinking and smoking. A new and troubling case of cyberbullying, this incident highlights the need to create tools to identify deepfakes.

Currently, there arent any apps to detect deepfakes, at least no apps dedicated to regular Internet users.

There is however a project made by a couple of University of Washington scientists, which aims to help you spot images that are actually computer-generated. WhichFaceIsReal.com puts actual photos of people side by side with deepfakes generated by Nvidias AI algorithm StyleGAN, which is able to create almost-perfect portraits of humans.

This exercise can teach you the tell-tale signs of AI-generated portraits, from weird backgrounds to teeth details.

You can also achieve the same results by watching some of the best deepfake videos released so far.

Thanks to Reddit, which was one of the first and biggest deepfake communities since the beginning, there is a wealth of Nicolas Cage deepfakes and each is funnier than the previous one.

Thought this actor had incredible range and a very long IMDB portfolio? Thats nothing compared with what the fake Nic Cage has been up to.

The same goes for the variety of Tom Cruise deepfakes. In one famous video, the fake Tom Cruise is discussing an encounter with the former Soviet-leader Mikhail Gorbachev. In another, hes playing golf.

All of them were viral sensations thanks to the @deeptomcruise account on TikTok. Theyre created by Belgian visual effects artist Chris Ume, who combined deepfake technology with a professional Tom Cruise impersonator to create some of the most convincing deepfakes yet.

Then, theres this impressive use of deepfake technology in documentaries. Director Morgan Neville deepfaked Anthony Bourdains voice to create a narration for the Roadrunner: A Film About Anthony Bourdain documentary.

When you hear the late chef saying You are successful, and I am successful, and Im wondering: Are you happy?, its actually a computer-generated voice.

Deepfake voices are actually among the first applications of deepfake technology and some of the hardest ones to detect from reality.

In 2019, author and academic Jordan Peterson, a controversial figure online, was targeted with this technology by a website that let anyone create a deepfake Jordan Peterson voice.

The neural network called NotJordanPeterson was trained to mimic the voice of Peterson and released online for anyone to use. Anyone could type anything and have the fake Peterson talk out loud, so the project had plenty of potential for misuse and could be arguably categorized as a very damaging form of cyberbullying. He responded by warning that deepfake artists and the misuse of this technology could make it so that we can no longer tell whats real and whats not.

In another, cuter project, a Jordan Peterson AI model was taught to sing Lose Yourself by Eminem.

Eminem was also the star of another popular deepfake project, one which took a social stance.

The same group who used AI to create a deepfake Eminem diss for Mark Zuckerberg, called Calamity AI, made a deepfake Eminem rap against the patriarchy, featuring a deepfake Kanye West. Notably, the lyrics were also created by AI. The project used Shortly.AI, a text generator based on the OpenAI GPT-3 project, and produced some pretty hilarious lyrics.

But if youre looking for the absolute best deepfake video, it belongs to the Star Wars universe.

With such a large fandom, it makes sense that Star Wars deepfakes would abound but this particular project stands heads and shoulders above all of them.

The Mandalorian Luke Skywalker Deepfake created by a famous YouTuber was so jaw-dropping, LucasFilm actually hired him.

Shamook, the creator of this Lucas Skywalker deepfake, demonstrated the full potential of de-aging technology and made a fake Mark Hamill indistinguishable from the real actor.

Certainly, deepfake technology is capable of transcending the limitations of space and time. It can de-age actors, bring beloved voices back from the dead and turn public personalities into whatever your imagination dreams up. As youve seen so far, its also a disruptive technology that has been misused time and time again, sometimes with scary consequences.

So, what do you think about this technology? Are you threatened by its implications or amazed by its potential? What types of safeguards or deepfake legislation should be created to distinguish the real from the computer-generated?

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What Deepfakes Can Do: The Best Videos And the Deepfake Apps Changing Reality - TechTheLead

5 tips to begin your career in the field of Data Science – India Today

August 14, 2021 | Author: admin

Data-driven job roles are growing extensively lately, encouraging the youth to educate themselves and skill up for different domains such as logistics, business intelligence, machine learning, data architecture, and data science. Data science is one such core job that has witnessed visible growth and given career opportunities to various professionals skilled in coding, analytics, maths, statistics, and data visualization. Opting for a career in data science seems lucrative today as it is in demand across retail, government, banking, media and communications, transportation, healthcare, education, and various other industries.

Although freshers are not expected to demonstrate immense expertise and work experience in the field, few important things could strengthen your chances of landing your first job as a data scientist.

Here are 5 tips that you must take into consideration to kick-start your career in the field of data science and make your application stand out.

Data scientists are expected to solve complex real-world problems based on data trends and patterns and thus, a combination of soft skills and specific profile or job-related skills is required. Understanding data science fundamentals, statistical skills, programming knowledge, predictive modeling, data visualization, data manipulation, and data analysis, is what you must excel in.

Basic knowledge of machine learning, deep learning, big data, and software engineering is also essential. You should also possess teamwork, time management, collaboration, communication, structured thinking, problem-solving, and management skills to justify your candidature for a fresher job in data science.

During your undergraduate period, if you are uncertain about your interests and are still on an exploration spree, you could enroll in beginner-friendly, short-term, and affordable data science training and acknowledge your confusion.

Whereas, in case you are extremely sure and enthusiastic about pursuing a career in data science, a 4-6 months long comprehensive specialisation would be a great choice. It will strengthen your skills and give you hands-on experience while you work on projects and deal with continuous practice and assessments.

You will also get an industry recognised certificate, placement assistance, and insightful sessions with industry experts that will validate your skills as a professional in the said domain.

An incomplete, casual, or unorganised portfolio could hamper your chances of getting hired. Your portfolio contains many elements other than your CV and cover letter and each of these must vouch for your candidature. Relevant to the data science profile you are applying for, build a digital professional portfolio that can be easily shared across with recruiters.

Showcase your familiarity with datasets, structures, statistics, models, and insights. Expand your portfolio by adding your career summary, personal information, list of skills, accomplishments, major and minor data science project details, resume, work samples, educational qualifications, professional development activities, and a reference list that could convince the recruiter why you are suitable for the role.

After learning and practicing the beginner-level concepts of data science by doing an internship would help you develop and refine your skill in data science. You get to apply your theoretical knowledge, build self-confidence, get the feel of working in the industry, increase your practical skills, and boost your motivation.

An internship is the best way to earn an extra income while you are still learning and polishing your job-specific skills, improve your CV, build a network, get a pre-placement offer or land jobs in other companies with the help of your seniors, gain work experience, and receive a recommendation from your employers.

Internships give you an edge and benefit over your co-applicants as recruiters usually gauge your ability to multitask, commit, own your work, and excel in your field based on your performance with the previous employers and your practical workability in technical fields like data science.

If you are an aspiring data scientist, it is essential for you to stay up to date with the latest industry trends, technological advancements, best practices, customers behavioral changes, and global activities in your field. Stay connected with the programming and data science communities, watch tutorials and take inspiration from experts work, read and share relevant articles, provide valuable feedback, and attend webinars and conferences by tech leaders.

Your understanding of general happenings, knowledge of the field, and familiarity with different data science leaders impress your recruiters and helps you get your favorite job opportunity. Moreover, this also helps you get recommendations to improve your work, build lifetime relationships with peers in the industry, get direct job opportunities, and potential leads of recruiters.

Courtesy: Internshala Trainings, e-learning platform to learn new-age skills from Internshala.

Read: 5 reasons why young law professionals are the social changemakers we need

Read: How law schools are preparing students for a competitive post-Covid market

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5 tips to begin your career in the field of Data Science - India Today

Fill the Application for These Top Data Scientist Jobs in MNCs Today – Analytics Insight

August 14, 2021 | Author: admin

Data scientist jobs in MNCs require applicants to be technically wise and use advanced tools.

Data scienceseems to be one of the few career options that survived unscathed during the pandemic. As companies rely more on big data and artificial intelligence, thedemand for data scientistsand otherdata scienceprofessions has drastically surged. Data will be generated every day, and in return, there will always be a need for someone to make sense of it. Although many companies open their door to welcome talenteddata scienceprofessionals, there is hype fordata scientistsworking in multi-national corporations (MNCs).Data scientist jobs in MNCsrequire applicants to be technically wise and use advanced tools to extract knowledge and insights from structured and unstructured data. Not just big tech companies, even MNCs in healthcare, communication, banking, education, etc. are also looking for skilleddata scientists. According to a report,top data scientist jobs are expected to grow by 16% through 2028. Besides, many MNCs are also readily providing work-from-home facilities fordata scienceprofessionals. Analytics Insight has listed topdata scientist jobs in MNCsthat aspirants can apply for today.

Location: Kochi, Kerala

Roles and Responsibilities: As a data scientist at IBM, the candidate will be working with a worldwide team on financial products. He/she will be participating in various solutions and come up with a common approach to be able to solve the issue by using machine learning capabilities. Thye should propose and design the solution and approach to solve a machine learning problem. The candidate needs to develop and code the solution from end to end and be able to integrate with existing offerings. As part of the worldwide team, they should also work on complex problems dealing with financial crimes.

Qualifications:

Applyherefor the job.

Location: Bangalore, Karnataka

Roles and Responsibilities: The data scientist will be part of the Enterprise IT, Information, and Data Science team and drive a strategic and actionable data science architecture to activate the needed business capabilities. He/she should work to deliver business use cases like Care Provider 360 initiative, Product Bundling, Value-Based pricing, Conversational AI, Indirect Trade partner classification, etc. They should ensure the strategic direction for data science capabilities for Philips is created and kept up to date on regular basis. The candidate should continuously evaluate the latest techniques in artificial intelligence, machine learning, robotics, statistical analysis, etc.

Qualifications:

Applyherefor the job.

Location(s): Chennai, Bengaluru

Roles and Responsibilities: As a data scientist at Cognizant, the candidate is expected to discover the information hidden in structured and unstructured data by applying data mining techniques, doing statistical analysis, and building high-quality prediction systems. He/she should evaluate and identify the right machine learning and data science techniques and toolsets to address a variety of predictive analytics problems. The candidate should be an individual contributor as well as a guide to fellow team members in the discovery, design, and development of analytical models.

Qualifications:

Applyherefor the job.

Location: Bengaluru

Roles and Responsibilities: LinkedIn is seeking to recruit a candidate who will work with a team of high-performing analytics, data science professionals, and product managers to identify business opportunities and optimize members experience at the company. He/she should do reporting and monitoring such as designing, creating, and automating reports and dashboards to track key business metrics in security product areas. They should be able to provide end-to-end deep-dive analytics. The candidate should develop and improve predictive models to optimize the user experience and operational efficiency.

Qualifications:

Applyherefor the job.

Location: Bengaluru

Roles and Responsibilities: Oracle is looking for a candidate who has prior knowledge in data science, predictive modeling, and validation. They are also expected to have PL, SQL, and SQL skills.

Qualification:

Applyherefor the job.

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Fill the Application for These Top Data Scientist Jobs in MNCs Today - Analytics Insight

What Stops Data Science from Scaling? Domino Data Lab to Host Enterprise MLOps Expert Virtual Panel on August 19th Exploring Common Failures and…

August 14, 2021 | Author: admin

SAN FRANCISCO, Aug. 11, 2021 /PRNewswire/ -- Domino Data Lab, provider of the leading Enterprise MLOps platform trusted by over 20% of the Fortune 100, will host a live virtual roundtable event on August 19 bringing together experts and veterans from across industries to discuss why most companies cannot succeed with data science at scale.

Featuring live commentary and Q&A with thought leaders and experts leading AI and ML at some of the most sophisticated data-driven companies in the world, the roundtable discussion will provide IT and data science leaders with insight and proven real-world methods they can use to effectively scale data science. Registration is free and available now.

97% of U.S. data executives say data science is crucial to maintain profitability and boost their company's bottom line, according to a recent study commissioned by Domino Data Lab.However, nearly as many say that flawed approaches to data science strategy, execution and staffing make achieving that goal difficult. This event will offer attendees a glimpse into the most common challenges enterprises face, and share real-world examples of how the most sophisticated companies in the world are solving them today with enterprise MLOps.

Moderated by distinguished speaker, author, and advisor, Tom Davenport, one of Harvard Business Review's most frequently published authors, the session will feature a panel of expert speakers including:

Throughout the open discussion, each speaker will bring their own point-of-view on implementing Enterprise MLOps practices into their operations. Attendees will also have an opportunity to ask the experts their opinions on best practices and the right processes they see work in practice to become a model-driven business.

"While companies have high expectations for data science and ML, their success requires deep changes across people, process and technology," said Nick Elprin, CEO at Domino Data Lab. "This group of speakers brings together decades of experience running data science organizations at scale to power model-driven businesses. I'm excited to discuss how Enterprise MLOps is helping companies like CSL Behring and Salesforce weave data science into the fabric of their business."

The virtual event takes place on Thursday, August 19th, 2021 from 9:00am - 10:00am PDT. To reserve your spot, register now.

Additional Resources

About Domino Data LabDomino Data Labpowers model-driven businesses with its leading Enterprise MLOps platform that accelerates the development and deployment of data science work while increasing collaboration and governance. More than 20 percent of the Fortune 100 count on Domino to help scale data science, turning it into a competitive advantage. Founded in 2013, Domino is backed by Sequoia Capital and other leading investors. For more information, visit dominodatalab.com.

SOURCE Domino Data Lab

http://www.dominodatalab.com

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What Stops Data Science from Scaling? Domino Data Lab to Host Enterprise MLOps Expert Virtual Panel on August 19th Exploring Common Failures and...

Learn the wonders of big data with this super-sized learning bundle for under $60 – The Next Web

August 14, 2021 | Author: admin

TLDR: The 2021 Big Data Certification Super Training Bundle includes 15 courses for unlocking everything there is to learn about data science, machine learning, artificial intelligence and more.

We all know what Big Data and the monumental impact of the massive data science explosion on every industry from banking to telecommunications to agriculture. But there are also areas where you might be surprised to learn data analysis is also driving change, including fields as traditionally non data-driven as psychology.

Clinicians are now studying conversations between psychologists and their patients to help detect changes in how those patients form thoughts and present themselves to others to help better treat their issues. What used to seem like the realm of science fiction, data science is now commonplace practice in dozens of industries.

With the training in The 2021 Big Data Certification Super Training Bundle ($59.99, over 90 percent off, from TNW Deals), students get hands-on training with the tools and tactics for understanding and using large data sets to unlock hidden truths found inside all those numbers.

This collection is a massive gathering of training, featuring 15 courses covering more than 113 hours of instruction in everything a budding data scientist needs to know to start using those number-crunching tools to help make informed decisions on almost anything.

Modern data science often begins and ends with the coding at the heart of it all, so learners will find a handful of courses in understanding and programming using the Python coding language. Courses like Deep Dive Into Python for Data Science and Python Data Science break it all down, showing new Python users the fundamentals of coding with the versatile and power language, from tools like NumPy and Pandas to using Python libraries to better organize and process data.

From that foundation, the training expands, introducing new components like using R programming data, mathematics, and other programs like Hadoop, Spark, Storm, and more to use data faster and more effectively.

Meanwhile, further coursework delves even deeper into more specialized uses of big data from its role in tracking stock performance to data visualization to its key role in facilitating both machine learning and artificial intelligence.

Each course in The 2021 Big Data Certification Super Training Bundle is valued at $200, but rather than a $3,000 package of training, this whole collection is available now for just $59.99.

Prices are subject to change

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Learn the wonders of big data with this super-sized learning bundle for under $60 - The Next Web

The rise of the autonomous data science teams – ETCIO.com

August 14, 2021 | Author: admin

By Dhrumil Dhakan

First, there was just a single IT team, then came along a data analytics team. But given the pace at which data science is evolving, it is high time that companies start looking at building autonomous data science teams to not just gain insight from data but also be able to take action based on it.

Autonomous data science teams are structured to find insights in a self-managing manner. The notion of team autonomy most common in agile methods of operations is to enable teams to make decisions of their own is central to todays world. Organizations need an Autonomous Data science team today because the need of using data is cross-functional.

But how important is this autonomy when building a data science team?

Srivastava further explains that for an organization, this co-dependence is of immense value as the nature of AI-powered products means that the core has to be customized to each customer. Building AI products requires a heavy data transformation and ML customization phase. This phase requires immense creativity and patience. Data science teams have to submerge themselves into the problem and be able to rapidly experiment, innovate and push the envelope. In such a situation, hierarchy and gates only serve to slow down progress and impede creativity.

While this may seem like the norm, it is quite difficult for data science teams to attain such autonomy as the freedom itself is not the problem but it is the ability to take action on them that becomes the problem. Most enterprises either struggle to trust leaving a business solution to a data science team or are not adequately prepared to transfer business context, constraints and problems to a data science team. This typically leads to a design-by-committee environment which leads to subpar progress and solutions.

This is not a one-way street as it takes an equally qualified team to trust them to take important decisions. While it is more than important to know the technical know-how, what makes for an ideal team member, when it comes to an autonomous data science team.

Khare believes that trust is a major part of what it takes to form a functional autonomous team, as the organization needs to know that, lets say, the insights are not shared before the necessary steps are taken.

Healthy communication and the ability to constantly ask questions and learn about the business problem adds to the skills required. Data scientists should be able to collaborate with other functional teams including product, engineering, marketing, support, customer success, operations, and sales. Data scientists have to work with stakeholders who do not share their technical depth and expertise, so they need to have a high degree of emotional intelligence, patience, and the ability to listen and educate.

An autonomous team does not exist in a vacuum, it operates within the confines of the organization, answering to their Chief Officer, and interacting with the other members of the firm on various topics. The team needs to be codependent with the other branches of the organization so function at its best.

So, how to build a culture that thrives on the autonomy of its data science team?

Ravi Pathak, Co-founder & CEO, Tatvic Analytics said, Having trust in your team is fundamental to building a sense of autonomy, however, leaders can take more proactive steps to help employees feel connected to their teams and other leaders in the organization. As a leader, you will always have to be available for guidance, help employees create strategic goals, give them the right tools to shine, and give increased flexibility where possible,

A robust team must be built that has the right levels of seniority, experience, and exposure. A mix of skill sets, backgrounds, experiences, and diversity in the team ensures that the team has access to a wide variety of ideas and thoughts. Clear objective metrics need to be defined and established and communicated to the team. This ensures that success criteria and governing KPIs are clear and understood enabling the team to self-determine their progress.

Another way leaders can develop a sense of autonomy tactically is by setting employees up with opportunities to grow, develop, and work on special projects. As they create and work on things outside of their immediate job role, their sense of autonomy increases, says Pathak on different ways to build the right culture in your organization for an autonomous team.

The most important benefit of having autonomy in a data science team is to drive a data-centric culture within the organization and establish a Learn-Plan-Test-Measure process in the other functions. It helps in creating more business leaders who are adaptable/flexible and can successfully drive organizational change.

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The rise of the autonomous data science teams - ETCIO.com

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What Stops Data Science from Scaling? Domino Data Lab to Host Enterprise MLOps Expert Virtual Panel on August 19th Exploring Common Failures and…

Learn the wonders of big data with this super-sized learning bundle for under $60 – The Next Web

The rise of the autonomous data science teams – ETCIO.com

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