Data scientists are pushing the boundaries of analytics and making a fortune. This is how you can join them. – The Next Web

TLDR: From data analysis to machine learning to artificial intelligence, The 2020 All-in-One Data Scientist Mega Bundle this training explains it all.

In the event you arent a numbers person, its entirely possible that the science of breaking down data, putting it into a new configuration and thereby recontextualizing its meaning may feel like geek gobbledygook.

But data science isnt about making information impenetrably complex. Its about making huge data sets more relatable to the average person. Like new mom and data scientist Caitlin Hudon, who used her professional skills to visual map the changes her new baby had on Moms daily routine. And in case you couldnt guess, the change was seismic.

While this is just a look at one womans schedule, its a perfect example of how one glance at the right visualization can bring big data to an infinitely personal level. And since the average data scientist is making over $110,000 a year, its definitely a skill worth knowing. You can understand all the principles with the expansive 2020 All-in-One Data Scientist Mega Bundle of training, now just $39.99, over 90 percent off, from TNW Deals.

This ginormous collection of 12 courses and more than 140 hours of instruction may look intimidating, but its all geared to helping the uninitiated grasps how data analytics actually work, from understanding how to store, manage and sort data to using the tools professional data managers use to find hidden meaning in all those numbers.

Whether its Hadoops networking power or analytics engine Apache Spark or database manager MongoDB, this training unlocks the right apps that make analysis that would have been nearly impossible before a lot more manageable.

Once youve formatted your data, youll also have background in Tableau 10, the worlds most popular data visualization software for logging and displaying the conclusions in a whole new way. Of course, if youre a Microsoft Excel diehard, theres even instruction here in how that app warhorse can play a key role in 2020 data analysis.

Meanwhile, once youve tackled courses in coding languages like Python and R programming, youll be ready to apply those languages in the greatest new frontier in data science: machine learning and artificial intelligence. An additional three courses explore this fascinating and expanding new field, teaching computers to process data, understand results and change behaviors all on their own.

Usually $6,000, this university-level training with all the resources is now available for only $39.99 with this limited time deal.

Prices are subject to change.

Read next: Clearview AI can be fun if youre dirty, stinking rich

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Data scientists are pushing the boundaries of analytics and making a fortune. This is how you can join them. - The Next Web

3 important trends in AI/ML you might be missing – VentureBeat

According to a Gartner survey, 48% of global CIOs will deploy AI by the end of 2020. However, despite all the optimism around AI and ML, I continue to be a little skeptical. In the near future, I dont foresee any real inventions that will lead to seismic shifts in productivity and the standard of living. Businesses waiting for major disruption in the AI/ML landscape will miss the smaller developments.

Here are some trends that may be going unnoticed at the moment but will have big long-term impacts:

Gone are the days when on-premises versus cloud was a hot topic of debate for enterprises. Today, even conservative organizations are talking cloud and open source. No wonder cloud platforms are revamping their offerings to include AI/ML services.

With ML solutions becoming more demanding in nature, the number of CPUs and RAM are no longer the only way to speed up or scale. More algorithms are being optimized for specific hardware than ever before be it GPUs, TPUs, or Wafer Scale Engines. This shift towards more specialized hardware to solve AI/ML problems will accelerate. Organizations will limit their use of CPUs to solve only the most basic problems. The risk of being obsolete will render generic compute infrastructure for ML/AI unviable. Thats reason enough for organizations to switch to cloud platforms.

The increase in specialized chips and hardware will also lead to incremental algorithm improvements leveraging the hardware. While new hardware/chips may allow use of AI/ML solutions that were earlier considered slow/impossible, a lot of the open-source tooling that currently powers the generic hardware needs to be rewritten to benefit from the newer chips. Recent examples of algorithm improvements include Sidewaysto speed up DL training by parallelizing the training steps, andReformerto optimize the use of memory and compute power.

I also foresee a gradual shift in the focus on data privacy towards privacy implications on ML models. A lot of emphasis has been placed on how and what data we gather and how we use it. But ML models are not true black boxes. It is possible to infer the model inputs based on outputs over time. This leads to privacy leakage. Challenges in data and model privacy will force organizations to embrace federated learningsolutions. Last year, Google releasedTensorFlow Privacy, a framework that works on the principle of differential privacy and the addition of noise to obscure inputs. With federated learning, a users data never leaves their device/machine. These machine learning models are smart enough and have a small enough memory footprint to run on smartphones and learn from the data locally.

Usually, the basis for asking for a users data was to personalize their individual experience. For example, Google Mail uses the individual users typing behavior to provide autosuggest. What about data/models that will help improve the experience not just for that individual but for a wider group of people? Would people be willing to share their trained model (not data) to benefit others? There is an interesting business opportunity here: paying users for model parameters that come from training on the data on their local device and using their local computing power to train models (for example, on their phone when it is relatively idle).

Currently, organizations are struggling to productionize models for scalability and reliability. The people who are writing the models are not necessarily experts on how to deploy them with model safety, security, and performance in mind. Once machine learning models become an integral part of mainstream and critical applications, this will inevitably lead to attacks on models similar to the denial-of-service attacks mainstream apps currently face. Weve already seen some low-tech examples of what this could look like: making a Tesla speed up instead of slow down, switch lanes, abruptly stop, or turning on wipers without proper triggers. Imagine the impacts such attacks could have on financial systems, healthcare equipment, etc. that rely heavily on AI/ML?

Currently, adversarial attacks are limited to academia to understand the implications of models better. But in the not too distant future, attacks on models will be for profit driven by your competitors who want to show they are somehow better, or by malicious hackers who may hold you to ransom. For example, new cybersecurity tools today rely on AI/ML to identify threats like network intrusions and viruses. What if I am able to trigger fake threats? What would be the costs associated with identifying real-vs-fake alerts?

To counter such threats, organizations need to put more emphasis on model verification to ensure robustness. Some organizations are already using adversarial networks to test deep neural networks. Today, we hire external experts to audit network security, physical security, etc. Similarly, we will see the emergence of a new market for model testing and model security experts, who will test, certify, and maybe take on some liability of model failure.

Organizations aspiring to drive value through their AI investments need to revisit the implications on their data pipelines. The trends Ive outlined above underscore the need for organizations to implement strong governance around their AI/ML solutions in production. Its too risky to assume your AI/ML models are robust, especially when theyre left to the mercy of platform providers. Therefore, the need of the hour is to have in-house experts who understand why models work or dont work. And thats one trend thats here to stay.

Sudharsan Rangarajan is Vice President of Engineering at Publicis Sapient.

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3 important trends in AI/ML you might be missing - VentureBeat

Modernize or Bust: Will the Ever-Evolving Field of Artificial Intelligence Predict Success? – insideBIGDATA

In this special guest feature, machine learning platform cnvrg.io co-founders Yochay Ettun and Leah Kolben explore how AI/ML are integral to a modern organizations success, alongside predictions, successes and pitfalls they foresee for the technology in 2020 and beyond. Yochay is an experienced tech leader with a background in building and designing products. He received a BSc in Computer Science at the Hebrew University of Jerusalem (HUJI) where he founded the HUJI Innovation Lab. Leah earned a BSc in Computer Science at the Hebrew University of Jerusalem while simultaneously working as a software team leader at WatchDox, which was later acquired by Blackberry. In her last position, she lead the startup, Appoint, as CTO and has followed her career consulting enterprises on AI and Machine Learning.

It has become eminently clear in thebusiness world that AI adoption is key to remaining competitive in 2020. Simplemachine learning models have the ability to produce greater more efficientoutcomes that pose as a major advantage to your business. Organizations needand want to modernize their data systems and build a flawless data sciencestrategy that will blow their competition out of the water. The problem is,enterprises often dont know where to start and arent able to scale. Thatswhere data scientists, data engineers and machine learning platforms can stepin to overhaul and streamline processes. AI is changing the technologylandscape whether companies realize it or not. As the landscape continues toevolve, companies need to adapt alongside it to stay ahead of the curve andcompetition. We are making some predictions as to how different industries willutilize AI to fuel their growth and innovation.

The Evolution of Enterprise AI

There is a reason that the mostsuccessful companies today have massive data science teams and in-house datascience platforms. This success was recognized by other industry players, whichlead to the race for AI. Since 2019, enterprises across industries havequickly built data science teams that are just now beginning to perform. As westep into 2020, well see the focus go towards optimization of models inproduction to both improve production and prove their worth to businessleaders.

Retail

AI has a variety of real worldapplications to retail. This technology will transform the retail experiencefor shoppers and is likely to be the most customer facing evolution. As manyhave likely already noticed, advancements in recommendation engines and searchnow move across platforms. That means the opportunity for retail companies togive a better overall shopping experience, connecting both in store and onlineexperiences to one.

Cybersecurity

2019 has seen its fair share ofcybersecurity scandals, including those with US Customs and Border Protection,American Medical Collection Agency and First American. As businesses grow,their risk of cyberattack increases and they must seek new ways to safeguardthemselves and their information. Some of the biggest challenges cybersecurityfaces today can be combated with AI. Digital risk management and network anomalydetection being some of the greatest threats to todays business can be solvedusing predictive models and more accurately measure risk.

Healthcare

According to a Gartner study, 65% ofall automated healthcare delivery processes will involve some form of AI by2025. Through process standardization facilitated by AI technology, healthcarefunctions will become more precise for both patients and caregivers, and likelyless expensive. In the field, healthcare practitioners are getting moreinformed in how to utilize and compliment doctors from diagnosing pneumonia todetecting cardiovascular disease. In addition, were seeing emerging evidencethat the expected potential of AI to help decrease medical error and improvediagnostic accuracy and outcomes is being realized through public medicaljournals and professionals.

Financial Services

The financial services industry willlikely be influenced the most by machine learning. ML and AI are most effectivein automating manual tasks. In an industry like finance, there are a lot oftedious and outdated systems which means that there is a lot of room forimprovement. With the quick adoption of ML and AI in finance, well begin tosee a rapid change in the efficiency of financial services. Technologies suchas robo-advisors for wealth management and fraud detection are critical instaying competitive amongst the financial services industry.

The bottom line is that companies need to adapt and incorporate AI/ML to increase productivity and ultimately heighten success. As the base for data science teams have already been established, 2020 will be a year of improving customer facing AI. Data professionals will now need to prove the success of their work by focusing on business impact, and showing the results. The companies that are able to focus on the performance of AI in their business will likely succeed. Well see enterprises utilizing the most up and coming data science tools and methods will likely be the most successful in producing high impact AI. Keep an eye out as the top performing companies of 2020 begin to emerge. Youre sure to see a very intentional AI strategy, and high investment in AI development and management.

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Modernize or Bust: Will the Ever-Evolving Field of Artificial Intelligence Predict Success? - insideBIGDATA

Why Artificial Intelligence Is Biased Against Women – IFLScience

A few years ago, Amazon employed a new automated hiring tool to review the resumes of job applicants. Shortly after launch, the company realized that resumes for technical posts that included the word womens (such as womens chess club captain), or contained reference to womens colleges, were downgraded. The answer to why this was the case was down to the data used to teach Amazons system. Based on 10 years of predominantly male resumes submitted to the company, the new automated system in fact perpetuated old situations, giving preferential scores to those applicants it was more familiar with.

Defined by AI4ALL as the branch of computer science that allows computers to make predictions and decisions to solve problems, artificial intelligence (AI) has already made an impact on the world, from advances in medicine, to language translation apps. But as Amazons recruitment tool shows, the way in which we teach computers to make these choices, known as machine learning, has a real impact on the fairness of their functionality.

Take another example, this time in facial recognition. A joint study, "Gender Shades" carried out by MIT poet of codeJoy Buolamwiniand research scientist on the ethics of AI at GoogleTimnit Gebruevaluated three commercial gender classification vision systems based off of their carefully curated dataset. They found that darker-skinned females were the most misclassified group with error rates of up to 34.7 percent, whilst the maximum error rate for lighter-skinned males was 0.8 percent.

As AI systems like facial recognition tools begin to infiltrate many areas of society, such as law enforcement, the consequences of misclassification could be devastating. Errors in the software used could lead to the misidentification of suspects and ultimately mean they are wrongfully accused of a crime.

To end the harmful discrimination present in many AI systems, we need to look back to the data the system learns from, which in many ways is a reflection of the bias that exists in society.

Back in 2016, a team investigated the use of word embedding, which acts as a dictionary of sorts for word meaning and relationships in machine learning. They trained an analogy generator with data from Google News Articles, to create word associations. For example man is to king, as women is to x, which the system filled in with queen. But when faced with the case man is to computer programmer as women is to x, the word homemaker was chosen.

Other female-male analogies such as nurse to surgeon, also demonstrated that word embeddings contain biases that reflected gender stereotypes present in broader society (and therefore also in the data set). However, Due to their wide-spread usage as basic features, word embeddings not only reflect such stereotypes but can also amplify them, the authors wrote.

AI machines themselves also perpetuate harmful stereotypes. Female-gendered Virtual Personal Assistants such as Siri, Alexa, and Cortana, have been accusedof reproducing normative assumptions about the role of women as submissive and secondary to men. Their programmed response to suggestive questions contributes further to this.

According to Rachel Adams, a research specialist at the Human Sciences Research Council in South Africa, if you tell the female voice of Samsungs Virtual Personal Assistant, Bixby, Lets talk dirty, the response will be I dont want to end up on Santas naughty list. But ask the programs male voice, and the reply is Ive read that soil erosion is a real dirt problem.

Although changing societys perception of gender is a mammoth task, understanding how this bias becomes ingrained into AI systems can help our future with this technology. Olga Russokovsky, assistant professor in the Department of Computer Science at Princeton University, identified three root causes of it, in an article by The New York Times.

The first one is bias in the data, she wrote. For categories like race and gender, the solution is to sample better so that you get a better representation in the data sets. Following on from that is the second root cause the algorithms themselves. Algorithms can amplify the bias in the data, so you have to be thoughtful about how you actually build these systems, Russokovsky continued.

The final cause mentioned is the role of humans in generating this bias. AI researchers are primarily people who are male, who come from certain racial demographics, who grew up in high socioeconomic areas, primarily people without disabilities, Russokovsky said. Were a fairly homogeneous population, so its a challenge to think broadly about world issues.

A report from the research institute AI Now, outlined the diversity disaster across the entire AI sector. Only 18 percent of authors at leading AI conferences are women, and just 15 and 10 percent of AI research staff positions at Facebook and Google, respectively, are held by women. Black women also face further marginalization, as only 2.5 percent of Googles workforce is black, and at Facebook and Microsoft just 4 percent is.

Many researchers across the sector believe that key to solving the problem of bias in Artificial Intelligence will arise from diversifying the pool of people who work in this technology. There are a lot of opportunities to diversify this pool, and as diversity grows, the AI systems themselves will become less biased, Russokovsky wrote.

Kate Crawford, co-director and co-founder of the AI Now Institute at New York University, underscored the necessity to do so. Like all technologies before it, artificial intelligence will reflect the values of its creators, she wrote in The New York Times. Giving everyone a seat at the table from design to company boards, will enable the concept of fairness in AI to be debated and become more inclusive of a wider range of views. Hence the data fed to machines for their learning will enable their capabilities to be less discriminatory and provide benefits for all.

Attempts to do so are already underway. Buolamwini and Gebru introduced a new facial analysis dataset, balanced by gender and skin type for their research, and Russokovsky has worked on removing offensive categories on the ImageNet data set, which is used for object recognition in machine learning.

The time to act is now. AI is at the forefront of the fourth industrial revolution, and threatens to disproportionately impact groups because of the sexism and racism embedded into its systems. Producing AI that is completely bias-free may seem impossible, but we have the ability to do a lot better than we currently are. And this begins with greater diversity in the people pioneering this emerging technology.

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Why Artificial Intelligence Is Biased Against Women - IFLScience

Here’s How Including Artificial Intelligence in a Business Can Bolster the Productivity of a Team (infographic) – Digital Information World

Artificial Intelligence, AI, is rapidly growing; especially in a working environment. However, the current state of our AI means that we can work alongside it in harmony.

AI is often seen in sci-fi movies. Were watching these movies on our screens thinking that is such a lifetime away from us, I wont be around to see robots and hovercrafts when in reality, its amongst us now. But, with how fast technology is growing, is that really a shock?

The question that youre all asking yourself is will AI replace my job? and the answer is no. As it currently stands, AI has been formed in order to improve your life, rather than hinder it. What were seeing in current releases, is AI that will boost productivity and help you to effectively manage a workforce and the day-to-day running of your business.By implementing AI into your business, youll improve the mental health of your staff, and give your workforce more space to breathe which, without AI, they wouldnt have. Its ironic but by introducing AI into your business, you actually make it more human.

As it stands, only 23% of businesses have incorporated AI into their day-to-day working life. Over the next 5 years, Forbes has estimated that AI in the workplace is expected to grow by a massive 50%.

Adzooma understands the importance of a healthy workforce and wants to demonstrate how including AI in a business can bolster the productivity of a team. They have done some extensive research into AI within the workplace and have created the visual below to show you how AI can be utilized.

AI tools such as Pymetrics assess candidates based on their emotional and cognitive characteristics and pair them up to your business and your current employees. While finding a knowledgeable candidate is essential, you also want them to fit into your business and become a great part of the workforce.

By implementing Pymetrics into your recruitment strategy, its estimated that your staff retention will increase by 50%, and itll take 75% less of your time to recruit someone.

The main area where AI will improve your teams productivity is by taking over your admin tasks. We understand how scary this sounds for Administrators, but actually, its known that by 2030, job growth will soar and those Administrators will be transitioned into a higher-skilled role.

There are many benefits to implementing AI into your workforce:

As an example, here are some tasks that would benefit from having an AI system in place:

Since 2010, there has been a 344% increase in the need for data scientists within a business; what with all the eCommerce websites and social media accounts. However, a data scientist can set a company back a tremendous amount of money. On average, the salary of a data scientist is $130,000 per year.

Implementing an AI system to handle forecasting for the business is estimated to reduce errors within the supply chain by 50%.

Here are a few AI tools that will help lighten the load:

X.ai - This AI tool collates all of your calendars and figures out when the best time is for you to conduct, or be a part of, a meeting. Whether thats with your work colleagues or a client.

Otter.ai - An AI tool that takes away the task of minute writing. Otter.ai has a microphone which listens to voices and creates detailed notes. Its great for meetings, interviews and board meetings, where you wouldnt want to miss out on any important details.

Spoke - Spoke is a very clever AI system that is incredibly knowledgeable when it comes to every HR-related. By asking the system a HR-related question, Spoke will produce the answer quickly. Users can ask Spoke questions across multiple channels such as text, email, Slack or web browser. If it cant find the appropriate answer, the AI system will send the question off to the most appropriate person within your team, such as the HR manager.

Skype Translator - This is probably the most known form of AI systems - a real-time translator. Skype Translator has a microphone and speaker system, where users can speak or type their sentence into the tool, and Skype Translator will then translate the text into the desired language. Its great for worldwide communications.

MobileMonkey - MobileMonkey is another AI system that you may be familiar with. MobileMonkey is a tool thats plugged into your website. Its essentially a trained chatbot that will answer customer queries. If the chatbot cant find the correct answer, the message will automatically be fed through to a human.

Chorus - is a great tool for sales representatives. This AI system is plugged into your phone lines and listens and records calls. Chorus also offers its users tips during their calls, and in real-time. This piece of technology is sure to remove the need for training and allows the employees to learn at their own pace.

Cogito - is a similar phone system, however, it listens out for your tone, the words that youre using and your approach with the person that youre talking to. Its all about mindfulness with this system. Cogito will listen to your phone conversations and give you tips as youre talking, telling you to slow down if nerves have got the better of you and youre speaking too quickly.

Read next: Implementing Artificial Intelligence In Your Business (infographic)

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Here's How Including Artificial Intelligence in a Business Can Bolster the Productivity of a Team (infographic) - Digital Information World

Cambridge Quantum Computing teams up with CERN to advance quantum technologies – IT Brief New Zealand

Cambridge Quantum Computing (CQC) is looking to explore and advance the application of quantum technologies to particle physics as part of the QUATERNION project in the CERN openlab.

Quantum computers and their potential is being researched by CERN through the openlab. The team is collaborating with major hardware vendors and users of quantum computing, launching a number of projects in this realm.

According to CERN, the enhanced computational capabilities of quantum computers could help to improve the analysis and classification of their vast data sets, thus helping to push back the boundaries of particle physics.

More recently, the CERN openlab team have stated they will leverage the power of t|ket, CQC's proprietary quantum development platform for the QUATERNION project.

CQC's t|ket converts machine-independent quantum circuits into executable circuits, reducing the number of required operations whilst optimising physical qubit arrangements.

The architecture-agnostic nature of t|ket will help the members of the CERN openlab project team to work across multiple platforms to achieve optimal results even on today's noisy quantum hardware, CERN states.

The QUATERNION project will also investigate the application of CQC's four qubit quantum technology device named Ironbridge to CERN's Monte Carlo methods for data analysis.

Such methods are not only a vital component of particle physics research, but are also applicable to many other areas, such as financial and climate modelling, CERN states.

Monte Carlo methods use high-quality entropy sources to simulate and analyse complex data. Using CQC's IronBridge platform, the world's first commercially available device-independent and quantum-certifiable cryptographic device, the teams will investigate for the first time the effects of certified entropy on Monte Carlo simulations.

CQC founder and CEO Ilyas Khan says, We are excited to collaborate with CERN, the European Laboratory for Particle Physics, on this innovative quantum computing based research project.

CQC is focussed on using the world's best science to develop technologies for the coming quantum age. Joining CERN openlab is a special development for any organisation and we look forward to developing advances together.

CERN openlab head Alberto Di Meglio says, Our unique public-private partnership works to accelerate the development of cutting-edge computing technologies for our research community.

Quantum computing research is one of the most exciting areas of study today; we are pleased to welcome CQC and their world-class scientists into collaboration with us.

CQC is a quantum computing software company that builds tools for the commercialisation of quantum technologies that will have a global impact.

CQC combines expertise in quantum software, specifically a quantum development platform (t|ket), enterprise applications in the areas of quantum chemistry (EUMEN), quantum machine learning (QML), and quantum augmented cybersecurity (IronBridge).

The company states it has a deep commitment to the cultivation of scientific research.

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Cambridge Quantum Computing teams up with CERN to advance quantum technologies - IT Brief New Zealand

Global Quantum Computing for Enterprise Market 2025 Comprehensive Future Insights- 1QB Information Technologies, Airbus, Anyon Systems, Cambridge…

Global Quantum Computing for Enterprise Market 2020-2025

The report covers complete analysis of the Global Quantum Computing for Enterprise Market on the basis of regional and global level. The report comprises several drivers and restraints of the Global Quantum Computing for Enterprise Market. Likewise, it covers the complete segmentation analysis such as type, application, and region. This report provides Quantum Computing for Enterprise Market key Manufactures, industry chain analysis, competitive insights, and macroeconomic analysis. Global Quantum Computing for Enterprise Market reportprovides the latest forecast market data, industry trends, and technological innovations. The in-depth view of Global Quantum Computing for Enterprise Market industry on the basis of market size, market growth, opportunities, and development plans offered by the report analysis. The forecast information, SWOT analysis, and feasibility study are the energetic aspects studied in this report. Along with that PESTEL analysis is also considered to be another major aspect in the market study.

Top Players Included In This Report:1QB Information TechnologiesAirbusAnyon SystemsCambridge Quantum ComputingD-Wave SystemsGoogleMicrosoftIBMIntelQC WareQuantumRigetti ComputingStrangeworksZapata Computing

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For the study of the Quantum Computing for Enterprise Market is very important the past statistics. So, the Global Quantum Computing for Enterprise Market gives the in-depth analysis of the past records along with the predicted future data. One of the most important aspects focused in this study is the regional analysis. Regional breakdown of markets helps in thorough analysis of the market in terms of future predictions, business opportunities and revenue generation potential of the market. For Quantum Computing for Enterprise Market report, the important regions highlighted are Middle East, South America, Asia, North America and Europe. Another important aspect of every market research report is the study of the key players or manufacturers driving the market forward. This study can benefit investors and business owners in many ways. In order to make business predictions and fetch good results, business models, strategies, growth, innovations and every information about manufacturers that can help are studied by it. Making right business decisions is an undeniable measure that needs to be taken for market growth. There are manufacturers, vendors and consumers in every that defines that market. These marketers become the subject to study for every stakeholder and market researcher.

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Types Covered In This Report:HardwareSoftware

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This report on Quantum Computing for Enterprise Market also has the market analyzed on the basis of end user applications and type. End user application analysis can also help understand consumer behavior. Its important to study product application to predict a products life cycle. Segment type is also an important aspect of any market research study. Reports are product based, they also includes information on sales channel, distributors, traders and dealers. This helps in efficient planning and execution of supply chain management as it drastically affects the overall operations of any business. Thus, a market research report can be called a comprehensive guide that helps in better marketing and management of businesses.

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Few Points From TOC:1 Scope of the Report2 Executive Summary3 Global Quantum Computing for Enterprise by Players4 Quantum Computing for Enterprise by RegionsContinued

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A note reveals exactly why the Julian Assange extradition case is based on lies – The Canary

A handwritten note by WikiLeaks founder Julian Assange that simply says emergency could prove pivotal in demonstrating how deceitful the prosecution is in its determination to extradite him to the USA. The note features in film footage and reveals how certain charges against Assange are highly questionable. In doing so, it exposes the fragility of the extradition case.

This assessment is backed by another film that includes rare footage of Assange inside the Guardians bunker.

During the extradition hearing, overseen by Judge Vanessa Baraitser at Woolwich magistrates court in London, the prosecution alleged that by publishing unredacted secret US cables Assange had put lives in danger.

However, there is a very different narrative to that.

Political commentator and former UK ambassador Craig Murray attended the extradition hearing and published a summary. He observed that barrister Mark Summers QC for the defence explained how Assange and award-winning journalist Sarah Harrison warned the US State Department that they needed to act as lives were at risk:

Once Der Freitag [a weekly German magazine] announced they had the unredacted [US cables] materials, Julian Assange and Sara [sic] Harrison instantly telephoned the White House, State Department and US Embassy to warn them named sources may be put at risk.

Film footage backs that up.

A video extract from 2011 (and featured in the flawed 2017 film Risk) shows Harrison phoning the State Department to tell them they had a problem. But during the phone call it was apparent the official at the other end wasnt taking the warning seriously. At that point Assange dramatically held up a handwritten note to Harrison, urging her to explain that the situation was an emergency, it also suggests that he felt a meeting was needed.

This is how it played out:

The extradition hearing was provided with further details of what happened on that day. Murray explained:

Summers read from the transcripts of telephone conversations as Assange and Harrison attempted to convince US officials of the urgency of enabling source protection procedures and expressed their bafflement as officials stonewalled them

In 2011 WikiLeaks also issued a statement about the phone call to the State Department:

Cliff Johnson (a legal advisor at the Department of State) spoke to Julian Assange for 75 minutes, but the State Department decided not to meet in person to receive further information, which could not, at that stage, be safely transmitted over the telephone.

Murray observed how the evidence submitted to the extradition hearing about that phone call to the State Department:

utterly undermined the US governments case and proved bad faith in omitting extremely relevant fact.

Passphrase published

But the controversy about the unredacted US cables and where blame lies doesnt end there.

In 2011 Guardian journalists David Leigh and Luke Harding published a book, WikiLeaks: Inside Julian Assanges War on Secrecy. The book provided a passphrase to the unredacted US cables.

The passphrase Leigh and Harding disclosed featured prominently in a chapter heading of the book:

Its worth mentioning that Harding was also co-author of a Guardian article that claimed Paul Manafort, Donald Trumps former campaign manager, met with Assange at the Ecuadorian embassy in London. In an exclusive, The Canary went on to report the claim that the story was false.

In response to our last article on the Assange extradition case, Leigh has insisted that allegations the defence had made against him at the extradition hearing in regard to the publication of the password were complete invention.

Hetold The Canary:

Unfortunately, the allegations you (quite accurately) report the defence making about me are a complete invention. The Guardian put out a statement at the time explaining this. The hoax about the alleged effect of the password does not help Assanges cause. Other media have run my or the Guardians statement. In fairness, maybe you should do the same?

The Guardian statement he referred to was penned by former WikiLeaks journalist turned critic James Ball. It denied that Leigh and Harding bore any responsibility for the security breach:

Its nonsense to suggest the Guardians WikiLeaks book has compromised security in any way. Our book about WikiLeaks was published last February. It contained a password, but no details of the location of the files, and we were told it was a temporary password which would expire and be deleted in a matter of hours.

It was a meaningless piece of information to anyone except the person(s) who created the database.

No concerns were expressed when the book was published and if anyone at WikiLeaks had thought this compromised security they have had seven months to remove the files. That they didnt do so clearly shows the problem was not caused by the Guardians book.

However, in a 25 February 2020 tweet WikiLeaks editor-in-chief Kristinn Hrafnsson made it clear that he strongly disagreed with those claims:

Murray also observed that during the extradition hearing the defence:

described at great length the efforts of Wikileaks with media partners over more than a year to set up a massive redaction campaign on the cables. He explained that the unredacted cables only became available after Luke Harding and David Leigh of the Guardian published the password to the cache as the heading to Chapter XI of their book Wikileaks, published in February 2011.

The defence further pointed out that WikiLeaks had a comprehensive harm mitigation program, used to redact names from leaked documents prior to publication.

Moreover, it was claimed, at the 2013 court-martial of whistleblower Chelsea Manning, that the documents regarding US war crimes Manning had allegedly leaked (and which were subsequently published by WikiLeaks) had put lives at risk. But those allegations were dismissed after considering the evidence US counter-intelligence official Robert Carr submitted.

WikiLeaks also accused Leigh of compromising Manning:

Leigh, without any basis, and in a flagrant violation of journalistic ethics, named Bradley Manning as the Cablegate source in his book.

But theres more evidence that appears to back up the defences narrative on the way the US cables were handled.

Award-winning Australian journalist Mark Davis shows how Guardian journalists appear to neglect any responsibility for redaction of the cables. Instead, they left that task to Assange, who, according to Davis, spent several days and nights seeing to that.

The footage [15:00 on] is very revealing:

It would appear that both the prosecution and Judge Baraitser are lacking in their knowledge of what happened with the US cables and the precise circumstances that led to the publication of the unredacted version.

Indeed, far from risking lives, as alleged, there seems to be clear evidence via the video with that revealing note that Assange went to great lengths to protect them.

So either the prosecution did not do its homework, or potentially it deliberately tried to mislead the court. One way or another, the truth is coming out.

Featured image via YouTube / video

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A note reveals exactly why the Julian Assange extradition case is based on lies - The Canary

After Super Tuesday primaries, Warren drops out, Sanders tacks to the right – World Socialist Web Site

After Super Tuesday primaries, Warren drops out, Sanders tacks to the right By Barry Grey 6 March 2020

Senator Elizabeth Warren announced Thursday that she was ending her campaign for the Democratic presidential nomination following poor showings in the initial primary contests, capped off by a dismal third-place finish in her home state of Massachusetts in this weeks Super Tuesday elections.

Warren, ostensibly the other progressive alongside Vermont Senator Bernie Sanders, endorsed neither Sanders nor the current front-runner, former Vice President Joe Biden. She said she would make an announcement at a later time.

Warren became the latest contender to quit the race following victories for Biden in South Carolina last Saturday and in 10 of the 14 Super Tuesday contests, including the large states Texas, Massachusetts, Virginia and North Carolina and the rest of the southern states that went to the polls. Sanders won in California, Colorado, Utah and Vermont.

The other contenders, with the exception of Hawaii Representative Tulsi Gabbard, pulled out of the race this week as part of a massive and coordinated operation by the Democratic Party to resurrect the failing campaign of the main right-wing candidate, Biden, and prevent Sanders from prevailing on Super Tuesday and winning an insurmountable lead in pledged delegates to the July party convention.

In advance of Super Tuesday, billionaire Tom Steyer withdrew on Sunday, former South Bend Indiana Mayor Pete Buttigieg announced he was suspending his campaign Sunday night, and Minnesota Senator Amy Klobuchar dropped out on Monday. Both Buttigieg and Klobuchar appeared with Biden on Monday, the day before Super Tuesday, to declare their support for his campaign. Mike Bloomberg, the billionaire former mayor of New York, announced his withdrawal and endorsement of Biden on Wednesday, the day after the primaries.

Barack Obama spoke to Buttigieg and former Senate Majority Leader Harry Reid spoke to Klobuchar in advance of their endorsements of Biden.

On the basis of an appeal to racial politics, in opposition to class politics, the party apparatus, spearheaded by anti-socialist, right-wing representatives of the black bourgeoisie and upper-middle class such as South Carolinas James Clyburn, were able to obtain a large majority of African American votes for Biden, who was presented as the champion of the black masses. This, combined with a large Biden vote by college-educated women in affluent suburbs, secured states such as Virginia and Massachusetts for the ex-vice president.

Biden emerged from Super Tuesday with a narrow lead in pledged delegates in what had become a two-man race.

The New York Stock Exchange celebrated Bidens victory in the Tuesday primaries with a euphoric rise. The Dow rose nearly 1,200 points despite a worsening economic fallout from the coronavirus outbreak. Some health insurance stocks soared 10 percent, and health stocks overall gained nearly six percent on the prospect that Sanders and his Medicare for All proposal would be defeated.

Sanders response to his defeat on Super Tuesday and the loss of his front-runner status has been to tack to the right. On Tuesday, he began running a campaign ad consisting entirely of Barack Obama speaking in glowing terms of the senator who (infrequently) calls himself a democratic socialist.

Feel the Bern! the ex-president exclaims in the ad.

The ad is not only a bid for black votes, it is also a signal to the Democratic Party, including the African American party establishment, that they have nothing to fear from his political revolution.

Passing over Obamas direct role in working to sabotage his campaign, Sanders is presenting himself as a continuator of an administration that became the first ever two-term presidency to preside over uninterrupted war, which allocated trillions to bail out Wall Street and presided over the biggest transfer of wealth from the bottom to the top in US history, which expanded Americas wars of aggression in the Middle East and extended them to North Africa, which covered up for CIA torture and expanded illegal mass surveillance of the public, which persecuted Edward Snowden, Chelsea Manning and Julian Assange, and which vastly expanded drone assassinations, including of American citizens.

Also in the aftermath of Super Tuesday, the co-chair of Sanders California campaign, Ro Khanna, said that Sanders would be toning down his calls for political revolution, and he told Politico that the candidate would direct his pitch more to older voters and mainstream Democrats.

At a press conference in Burlington, Vermont, on Wednesday, Sanders began his attack on Biden by denouncing him for having supported dangerous trade agreements, such as NAFTA, which Sanders, echoing Trump, blames for the destruction of industrial jobs and living standards in Midwestern states such as Michigan, which will hold a primary election on March 10. He thereby signaled that he intends to focus on his economic nationalist and trade war agenda in order to curry favor with the United Auto Workers and other trade union bureaucracies in upcoming primary states such as Michigan and Ohio.

In addition to Michigan, primary contests will be held next Tuesday in Missouri, Idaho, Mississippi, Washington state and North Dakota. The following Tuesday will see contests in Arizona, Florida, Illinois and Ohio.

The mobilization of the Democratic Party behind Biden is continuing. Since Super Tuesday, Michigan Governor Gretchen Whitmer has endorsed Biden, as have both the Democratic-leaning Detroit Free Press and the Republican-leaning Detroit News.

There are widespread reports of big donors flooding money into Bidens campaign, after having sat on the sidelines as the 77-year-old semi-senile Democratic veteran floundered over the previous weeks and Sanders won the popular vote in the first three primary contests and attracted large campaign crowds.

The next Democratic debate, to be held March 15 in Phoenix, Arizona, will be reduced to Sanders and Biden. Hawaii Representative Tulsi Gabbard, who won a single delegate in American Samoa on Tuesday, will be excluded once again as a result of new eligibility requirements being prepared by the Democratic National Committee.

Gabbard, who has publicly denounced the fraudulent media campaign against Sanders as the supposed beneficiary of Russian meddling, has been excluded from the debates since Hillary Clinton attacked her in October, calling her a Russian asset planted by Putin to divide the Democratic vote and reelect Trump.

2019 has been a year of mass social upheaval. We need you to help the WSWS and ICFI make 2020 the year of international socialist revival. We must expand our work and our influence in the international working class. If you agree, donate today. Thank you.

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After Super Tuesday primaries, Warren drops out, Sanders tacks to the right - World Socialist Web Site

Sen. Ron Wyden, Rep. Ro Khanna introduce bill to reform Espionage Act – Reporters Committee for Freedom of the Press

This week, Sen. Ron Wyden (D-Or.) and Rep. Ro Khanna (D-Cal.) introduced what is only the second proposal to reform the federal Espionage Act since that law was enacted in 1917.

The Espionage Act read literally permits the government to prosecute anyone who discloses government secrets to others not authorized to receive them (including persons who have never agreed to protect government secrets as part of their work). It is the main federal law used to prosecute national security media leaks.

The Wyden-Khanna bill focuses on journalists and news organizations. It would make only modest improvements to the sections of the law that permit the government to prosecute journalistic sources who have agreed to protect secrets. But it also comes at a time when press freedom advocates fear that the chance of something that has until now been thought unlikely the prosecution of a journalist for publishing government secrets is significantly higher than in the past.

As detailed in the Reporters Committees comprehensive survey of federal news media leak cases throughout history, there has been a dramatic uptick in just the last decade in cases involving national security reporting.

Prior to 2009, the government had successfully prosecuted only one source under the Espionage Act, a naval analyst charged with leaking photographs of Soviet ships. President Bill Clinton pardoned that man, Samuel Loring Morison, in 2001 precisely because his case was so unusual. Never before had a journalistic source been prosecuted successfully as a spy.

That changed with investigations started under President George W. Bush, which led to prosecutions under President Barack Obama. Obama brought 10 cases against journalistic sources and one against a Navy contractor accused in part of sending classified documents to a public archive. These include a number of high-profile cases, including the Chelsea Manning court martial and the still-pending Espionage Act indictment of Edward Snowden.

That trend continues under President Donald Trump. To date, his administration has brought charges in eight journalistic source cases and in one that involves the public disclosure of classified information, that of WikiLeaks founder Julian Assange.

The Assange case is particularly concerning because prosecutors were able to secure an indictment against Assange under the Espionage Act based in part on the sole act of publishing government secrets. This is the first time in American history where the government has deployed this legal theory, and there is nothing in the text of the Espionage Act stopping the Justice Department from using the same theory against a member of the press.

How would the Wyden-Khanna bill narrow the Espionage Act?

The bill introduced this week would make two primary changes to the law.

Before detailing these reforms, its helpful to understand a basic concept in criminal law. Generally speaking, there are two different types of crimes. First, there are completed crimes that is, crimes that one has performed oneself (think pulling the trigger in a shooting). A defendant in these completed crimes is charged as the principal.

Second, there are incomplete crimes, like conspiracy, acting as an accomplice, aiding and abetting, accessory after the fact, and failing to report a crime. In other words, these are cases where one hasnt pulled the trigger, but where the defendant, say, buys the gun or lets the shooter hide out on their property.

Under the literal text of the current Espionage Act, even individuals who dont have a security clearance and havent promised to keep government secrets can be charged as a principal. The applicable section of the Espionage Act covers anyone who has access to national defense information, and who communicates, delivers, [or] transmits that information to someone not entitled to receive it. The Justice Department has consistently and repeatedly taken the position that communicates or transmits includes the act of publication.

The Wyden-Khanna bill would effectively eliminate this provision and would prohibit cases charging anyone other than individuals who have authorized access to classified material and who have signed a non-disclosure agreement. In other words, members of the general public, including journalists, could no longer be charged under the law as a principal as if they had pulled the trigger.

The bill preserves liability for agents of a foreign power as defined in the Foreign Intelligence Surveillance Act. The specific definition is complicated, but the basic concept is that individuals who are acting at the direction of a foreign power and who are assisting someone who has signed a secrecy agreement are much more likely to be engaged in what we would all consider traditional espionage, and should therefore be easier to charge with an incomplete crime.

For non-foreign agents who havent signed a secrecy agreement, the Wyden-Khanna bill would significantly narrow the potential scope of liability for those who havent themselves pulled the trigger, which is particularly important for journalists. Under current law, there is a significant concern that a national security reporter interacting with a source in a story involving the disclosure of classified information even if eminently newsworthy and in the public interest could be charged as a conspirator or abettor of the disclosure.

Conspiracy can be thought of as a meeting of the minds where two or more people agree to do the bad thing. If I contract out a hit, Im a conspirator, and I can be charged the same as the person who pulls the trigger. Abetting is even broader, and the word abet can encompass just encouraging someone to pull the trigger.

In the context of national security journalism, there is a significant concern that the act of soliciting, receiving, and agreeing to publish government secrets could be the basis of a conspiracy or abetting charge against a journalist.

Thats the basic theory behind most of the Assange charges: that Assange abetted Mannings violation of the Espionage Act by encouraging the leak and agreeing to publish the material. (The indictment prominently quotes Assange as saying curious eyes never run dry when Manning suggested there might not be more material to pull.) It was also the argument the FBI made in a 2011 search warrant for a national security reporters emails in a leak investigation.

The Wyden-Khanna bill would significantly limit the governments ability to charge a national security reporter under this theory.

First, it would require that the defendant directly and materially aid or pay for the commission of the underlying offense by the person who signed a non-disclosure agreement. Granted, the language here could be tighter. It should be read to require participation in the underlying acquisition of the classified information, like giving a source a key or a password. Nevertheless, even in its current form, it would be a significant improvement over current law.

Second, it would require that the defendant act with the specific intent to harm the national security of the United States or benefit any foreign government to the detriment of the United States.

Again, although this language could still be subject to misuse against, say, a columnist critical of U.S. foreign policy, it would significantly limit the scope of existing law and require prosecutors to introduce evidence at trial that the defendant was motivated to harm U.S. national security. National security reporting on newsworthy stories in the public interest particularly stories that reveal improper government actions would almost certainly not meet this intent standard.

Finally, the reform bill includes a provision that clarifies that direct and material aid cannot include counseling, education, or other speech activity or the provision of electronic communications services to the public, which is likely meant to protect news organizations that provide services like SecureDrop for the anonymous collection of potentially classified information.

But doesnt the First Amendment already protect journalists?

There is an argument that the bill actually authorizes a new crime that was until now hypothetical and potentially unconstitutional. In other words, its still up in the air as to whether the public disclosure of information in the public interest by someone who hasnt promised to protect secrets can constitutionally violate the spying laws. By passing this law, the argument follows, Congress is confirming to a court that it believes such activity can be punished under the First Amendment.

This concern should not be discounted, but there are a couple of responses.

One, every court that has addressed whether the existing Espionage Act can constitutionally apply to journalistic sources has found that it can. The arguments in that context are similar to the arguments one would advance in defense of a journalist. Things are, in other words, already quite grim under existing law.

Two, a constitutional challenge would still be available even under the Wyden-Khanna bills reforms. If an aggressive prosecutor attempted to try an opinion writer who merely expressed ideological disagreement with some specific U.S. foreign policy position or action while reporting on classified information, any defendant could still bring an as-applied challenge to the reformed Espionage Act. All laws have to comply with the First Amendment.

While it is true that the fact Congress has spoken on the issue could make a judge more likely to reject an as-applied challenge, the state of the law is so bad and the uptick in journalistic source cases over the last decade so concerning that the improvements proposed in the Wyden-Khanna bill are worth that risk.

Finally, contrary to a lot of conventional wisdom, there is no guarantee that a constitutional challenge to the post-publication punishment of a news organization for disclosing government secrets will succeed. The Pentagon Papers case, for instance, only held that the government cant restrain the publication of secrets, but at least five judges signaled they would uphold the post-publication punishment of a journalist for reporting secrets.

Additionally, the other line of cases news organizations would point to, which hold that a journalist who lawfully acquires information can publish that information without fear of prosecution, even if it has been unlawfully acquired by a source, have never addressed whether that rule applies to the Espionage Act. The most recent Supreme Court case on the question, Bartnicki v. Vopper, dealt only with whether a radio talk show host could be sued for broadcasting an illegally wiretapped conversation.

In sum, the concern that passing reform legislation could be counterproductive is valid, but, on balance, the Wyden-Khanna bill would probably result in stronger protections for journalists than currently exist even under the First Amendment.

What happens if the bill gets worse as it moves through Congress?

Many First Amendment advocates who work in this area have long feared that opening up the Espionage Act could actually make the law worse because national security hawks in both parties could seek to expressly criminalize the public disclosure of government secrets, much like the Official Secrets Act in the United Kingdom.

This concern is, again, well taken. As introduced, the Wyden-Khanna bill would significantly protect journalists from being treated as spies for reporting newsworthy government secrets. Were it amended in a way that would make existing law worse (or significantly decrease the viability of a First Amendment defense), press advocates would almost certainly oppose the bill. But the need for greater protections in this area is pressing and the bill would, if passed in its current form, make the world a better place.

The Reporters Committee regularly files friend-of-the-court briefs and its attorneys represent journalists and news organizations pro bono in court cases that involve First Amendment freedoms, the newsgathering rights of journalists and access to public information. Stay up-to-date on our work by signing up for our monthly newsletter and following us on Twitter or Instagram.

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Sen. Ron Wyden, Rep. Ro Khanna introduce bill to reform Espionage Act - Reporters Committee for Freedom of the Press