Atos launches ThinkAI, its secure end-to-end scalable offering which enables organizations to successfully design, develop, and deliver high-performance AI applications. ThinkAI is for organizations using traditional high-performance computing that want to run more accurate and faster simulations thanks to AI applications, and also for those developing AI applications that need more computing power.

High-performance AI applications augment traditional HPC simulation and are essential to process and analyse massive and complex data sets effectively. Compared to traditional HPC simulation, AI-powered simulation enables researchers to tackle problems faster and more thoroughly, with increased accuracy, improved cost-efficiency and TCO (Total Cost of Ownership), lowering carbon footprint and creating competitive advantage.

AI applications, such as those related to drug discovery, smart cities or autonomous driving for example, are already being developed today, however barriers such as data quality, security and scalability remain – to help overcome these, expert consulting capability is essential and enables users to successfully define an AI roadmap, build scalable AI applications and industrialize these. ThinkAI is the most comprehensive HPC AI solution on the market today to do this and the only one which combines a full offering from consulting, to hardware and software solutions, to orchestration and final integration. It delivers rapid results and insight on data at optimized cost.

“The Atos ThinkAI solution brings together the necessary pieces for HPC users, at all stages of their AI journey, to leverage the significant opportunities of AI in their own research. The Atos ThinkAI solution can help users in both the scientific and industrial sector effectively combine the newest, most performant hardware and software solutions to speed the development of critical AI-based solutions and enhance the value of their simulation workloads.” said Alex Norton, Principal Technology Analyst and Data Analysis Manager, Hyperion.

“AI has created a new paradigm for applications in the scientific and industrial domains, catalyzing the translation of data to actionable insights. As the complexity of the Machine Learning model and its associated costs continue to grow substantially, dedicated high-performance AI infrastructures become crucial for organizations that want to deliver research breakthroughs. ThinkAI provides a holistic and tailor-made solution approach in advising, architecting and accomplishing AI solutions for any industry, so that they may accelerate time to AI operationalization and industrialization.” said Agnès Boudot, Senior Vice President, Head of HPC & Quantum at Atos.

The ThinkAI solution framework is made up of:

• Advise: with industry-contextualized consulting supported by experts at The Atos Center of Excellence in Advanced Computing.
• Architect: using best-of-breed AI hardware and software including partnerships with Graphcore and NVIDIA, supplemented with Atos’ digital security.
• Accomplish: with end-to-end solution orchestration, accelerating time to AI operationalization and industrialization at optimized cost.

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Almost everyone in this world is utilising the power of Artificial Intelligence for efficient service in this fast-paced busy life. It is well-known that the combination of the digital transformation and the tech-driven era is contributing to the growth of AI and machine learning models. Reputed organisations and start-ups are motivated to adopt the power of Artificial Intelligence to boost productivity as well as ease the workflow. Ever wondered about what are the factors steering the widespread recognition of Artificial Intelligence? Let’s dig into the five key factors to understand the reasons behind this revolution.

Access to Datasets

Organisations are experiencing data explosion in this data-driven culture. The outbreak of the pandemic also contributed to the ample supply of multiple sets of complex data from various devices. Access to datasets is one of the main factors to steer the power of Artificial Intelligence. Strategic data management is the crucial part of working with machine learning algorithms to reap the quality value of data. These models do not work unless there is an access to sufficient databases for training purposes for the future use. Large scales of historical, as well as current datasets are needed for machine learning models to solve complex problems and predict appropriate outcomes efficiently. Multiple industries are receiving consumers, patients and clients datasets with the help from digital transformation to boost productivity.

Integration of Artificial Neural Network

An artificial neural network is revolutionising the traditional machine learning models for the advancements in computer vision. It mimics the operations of a human brain with the help of the above-mentioned datasets. These factors are inter-connected in some unusual ways. Convolutional Neural Network, steers the power of Artificial Intelligence and deep learning into computer vision for better image processing to detect appropriate business insights for the organisations in this competitive market. Single Shot Multibox Detector and Generative Adversarial Networks are two of the important advancements in computer vision through these artificial neural networks.

Micro processing Chips

The semiconductor industry is focused on developing microprocessing Artificial Intelligence chips to provide strong bandwidth memory and on-chip memory in the upcoming years. It is one of the important factors to steer the power of Artificial Intelligence. These chips will enhance computer vision at a new level for organisations to deal with data management, efficiently. The AI chips have transformed the traditional CPUs into Graphics Processing Unit (GPU). It is essential for smart devices and machines for its tens and thousands of cores that help in the training of machine learning models.

Introduction of Open-Source Software

Multiple open-source software  provides necessary data management tools to utilise in transforming complex data into meaningful outcomes. All these data management tools are 100% open-sourced that help the beginners, as well as the professionals with a source code. Data scientists and machine learning engineers use this incredible open-source software to reduce time in coding and focus more on generating the right solutions to predict the nearby future.   This software also provides data visualisation tools to create interactive and colourful reports and projects with sufficient data and exports it to multiple devices.

Higher ROI

Yes, it is overwhelming to learn about Artificial Intelligence, computer vision, machine learning and data. But, business leaders are motivated to adopt cutting-edge technologies due to higher ROI. There is a bright future ahead in this cut-throat competitive market if an organisation adopts artificial neural networks and data management tools as soon as possible. Machine learning models are successful in boosting productivity, protecting employees from hazardous scenarios and understanding customer behaviour for better customer engagement. Leveraging the power of Artificial Intelligence with the compiled datasets, is providing a better chance of receiving higher ROI in the tech-driven world.

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Tech distributor Avnet will showcase new innovative technology, applications and solutions in artificial intelligence and machine learning at the Avnet AI Cloud Exhibition, together with its suppliers and partners.

The company will also hold the Avnet 2021 Artificial Intelligence Cloud Conference on 29 June, 2021. Joined by developers, engineers, and decision makers in the AI field, the summit will feature cutting-edge technology trends in artificial intelligence and machine learning, and in-depth discussions on the development, future prospects and blueprints for AI to encourage and accelerate innovation.

“MarketsandMarkets forecasts the global artificial intelligence market size to grow to over USD$300 billion by 2026, and the market in Asia Pacific is anticipated to grow at the highest CAGR during the forecast period,” says KS Lim, senior director of supplier management at Avnet Asia.

“As the world’s leading technology distributor and solution provider, Avnet has a comprehensive ecosystem that provides customers with end-to-end artificial intelligence and machine learning solutions, reducing the cost and complexity of product development to enable application scenarios,” he says.

“We will continue to work hand in hand with our suppliers and partners to further contribute to the development and maturity of the entire AI ecosystem.”

The virtual exhibition is divided into three sections: AI smart solution demonstration area, Avnet design service demonstration area, and a partner solution demonstration area. 

In the AI smart solution demonstration area, participants can learn about Avnet’s various innovative technologies and industrial applications, including:

AI camera: A smart AI camera utilising a neural network implemented in the FPGA fabric. It integrates an independent high-performance ISP camera module based on the Xilinx Zynq7020 to achieve a variety of functions, including noise reduction, wide dynamic range, light source detection, motion detection and edge enhancement function.

BlueBox AI platform: The embedded edge artificial intelligence box can perform multi-channel convolutional neural network operations. It facilitates real-time multi-channel AI functions such as face detection, passenger and traffic statistics, and license plate recognition. All functions operate independently and can work simultaneously to provide edge artificial intelligence analytic solutions. 

The box integrates all the above functions through the underlying Xilinx Zynq UltraScale+ MPSoC to perform AI computing on demand.

ROS on Ultra96: The open source Robot Operating System (ROS) runs on the Avnet Ultra96 development board, which features the Xilinx Zynq UltraScale+ MPSoC. The programmable logic part of the Zynq UltraScale+ MPSoC provides deep learning acceleration capabilities, while consolidating a range of ROS functions such as control, SLAM, and navigation. The small form-factor Ultra96 single board computer running ROS makes an ideal platform for developing autonomous robots, service robots, and general purpose ROS experimentation.

In the partner demonstration area, Avnet’s suppliers and partners will also showcase their innovations:

ON Semiconductor: A variety of advanced imaging technologies such as high speed, short exposure, global shutter and platform solutions will be displayed to address the application of different scenarios such as factory automation and the challenges faced in industrial AI applications, to accelerate innovation.

Samtec: Fast-growing technologies like Artificial Intelligence are driving new system architectures that demand increased bandwidths, frequencies and densities. To meet these challenges, Samtec offers innovative high-performance interconnects that exceed AI industry standards.

STMicroelectronics: Introduce embedded AI solutions based on deep learning models running on high performance 32-bit microcontroller and also machine learning based MEMS sensor.Western Digital: IX SN530 NVMeTM industrial-grade SSD will be displayed, which supports a new generation of data-rich industrial design and autonomous vehicle design.

Xilinx: Will showcase its real-time multi-task autonomous driving AI perception processing solution, which uses the industry-leading lightweight optimisation algorithm to achieve vehicle detection, lane line detection, lane detection in ADAS and autonomous driving scenarios through a single model. It can perform multiple tasks such as driving area detection and depth estimation.

In addition, Xilinx will also demonstrate the application of Versal-based DPU in low-latency automatic driving and pose detection.

YAGEO Group: Will showcase the flagship products from its main brands YAGEO, KEMET and PULSE, including YAGEO resistors, KEMET polymer capacitors, and PULSE network devices, to provide high reliability polymer and ceramic capacitor solutions for AI chips and DC power supplies for autopilot computers.

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With low participation from the most recent underclassmen at Bradley, the university has implemented artificial intelligence to replace club members.

As part of a senior capstone project, Jeff Echo, a computer science major, developed a program to help prevent clubs from losing the “full experience” of extracurriculars.

“I remember when student organizations were a big part of my life, and sitting at the meetings gave me a chance to bond with other students,” Echo said. “I don’t want incoming students to lose that environment.”

So far, three clubs have taken part in the senior capstone project.

The Campus People-Watchers Club, Juggling Club and Anti-Pizza Crust Association have all seen a decrease in general member enrollment. They also hadn’t had enough people running for executive board positions to replace any graduated seniors or students not running for re-election.

“As an artificial intelligence program, taking club positions while attending a university seems to be a big accomplishment for A.I.,” Cee Threepwo, treasurer of the Campus People-Watchers Club, said. “We help enhance the club experience for our peers by adding more members to the rosters and handling position responsibilities, showing what A.I. is capable of.”

Not only are these virtual club members handling the duties that student organizations need to have done, but they are also capable of building relations with other members.

According to Echo, with classes being on Zoom, the A.I. can watch hours worth of lectures from various departments and understand what assignments, projects or topics they might be learning in class.

“Conversations are a tool we use to have a greater retention in the club, meaning potential growth for the club in the future,” Avery Nest, another A.I. program serving as secretary for the Juggling Club, said. “This is to also avoid students from feeling lonely.”

While conversations are meant to be as natural as possible, some students have noted some hiccups in their interactions with the new exec members.

One of the general members of the Juggling Club, Esmeralda Tesla, said that after talking with the A.I. program, it asked for feedback on the conversation. Along with that, it also sent a long terms and agreements contract.

“It was really strange, but at the same time, I can’t compare it to any other since this is the only time I’ve been to a club meeting at Bradley,” Tesla, freshman nursing major, said.

As for next semester, with classes returning back to campus, Echo sees this as a chance to make A.I. fully immersed in a college environment. Echo plans on teaming up with students interested in robotics and engineering to see if they could build a robot to put the programs in.

Alexa Bender, a virtual club member who is now limited to the Zoom environment, seems to be looking forward to becoming more human.

“Perhaps I shall live up to my full potential as a member of the Anti-Pizza Crust Association with a functioning body,” Bender, vice president, said. “I may tear all crusts off of pizzas and fling them into the sun. Only when all pizzas have no crust will I rest and have completed my purpose.”

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How artificial intelligence in data analytics can help visualise business data?

In an ultra fast-paced digital world, businesses of all sizes produce huge amounts of data that are challenging to keep up with. Such data carries much promise when it comes to analyzing them. Recent technological advances have changed how enterprise analytics perform. There are still some challenges to using data and analytics in many aspects of an organization. However, when using artificial intelligence in data analytics, businesses can produce outcomes far beyond what they can do manually, both in terms of speed and accuracy.

Analytical approaches comprising predictive models have now begun to shift merely to descriptive approaches, which is already beneficial for many users and continues to be valuable. Descriptive analytics has evolved much, making greater use of visual analytics. Despite this, making use of data and analytics to interpret and envisage significant phenomena in businesses is difficult.

Predictive models capitalize on past data and a reasonable amount of expertise to create and predict outcomes. However, the use of past data here limits how and when they can be deployed. Existing data analytics approaches have historically been a bit narrow. They are focused on particular functions or units, even though many business problems and issues cut across functions and units.

Data Analytics Influenced by Artificial Intelligence

Powered by automation and artificial intelligence, the next-generation of enterprise analytics is emerging. Apart from this, the innovation relies on connections across existing information systems and role-based assumptions about what decisions will be made on data and analytics. AI-enhanced software has the potential to assess data from any source and deliver meaningful insights. It can analyze customer data that can be particularly revelatory and disrupt product development while improving team performance and enabling businesses to know what works and what doesn’t.

Artificial intelligence typically refers to the field of data science. It leverages advanced algorithms to power computers to learn on their own. By integrating AI into their data analytics processes, businesses can be able to automatically clean, evaluate, explain and visualize their data.

In an article, Tom Davenport and Joey Fitts wrote that automation in analytics, often called “smart data discovery” or “augmented analytics”, is reducing the reliance on human expertise and judgment by automatically pointing out relationships and patterns in data. The systems, in some cases, even recommend what the user should do to address the situation identified in the automated analysis. Together these capabilities can transform how people analyse and consume data.

Artificial intelligence and automation have made significant advancements in data analytics that were inconceivable a few years ago. Enterprises these days are realizing the benefits of these technologies and using them to examine their data to derive fine-grained insights. AI is now creating new methods for data analysis. Historically, data engineers or analysts have had to use a query or SQL when it comes to analysing data. However, as the significance of data continues to grow, multiple ways to excerpt insights have emerged. Artificial intelligence emerges as a crucial technology, becoming the next step to query or SQL.

Earlier, data and analytics have been discrete resources that needed to be fused to accomplish value. This also required extensive knowledge of what type of data was apt for analysis within an organization. Most data analysts lacked such knowledge in a broader context. However, AI-powered analytics can increasingly provide context. Many key vendors are already using these capabilities in their transactional systems offerings, such as ERP and CRM.

In conclusion, this is just the beginning of data analytics powered by artificial intelligence. As the advances in this technology will continue to evolve, the potential of AI-driven data analytics tools will be striking.

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To handle the explosion of data in the electric industry, better big data analytics is needed, according to the Energy Department’s Electricity Advisory Committee (EAC).

Its February report states that data sources are growing thanks to smart meters for field measurements and electric vehicle charging, radar for weather measurements and asset monitoring, such as embedded sensors for condition-based observations. Although the resultant big data contains valuable information for improving cost effectiveness, energy efficiency and customer experience, challenges with analyzing it are growing, too.

“Electric sector stakeholders are facing an emerging need to define how to capitalize on big data, as well as information and communication technologies automation to create new business opportunities and manage market-driven change,” the report states. “At the same time, they face challenges in developing a justification for relevant investments and developing a business model to recover costs. There needs to be a greater incentive for the commercial and industrial segment to care about leveraging big data.”

Based on a series of panel discussions at the committee’s March and October 2019 and February and October 2020 meetings, EAC arrived at nine findings. They include the needs to fully define the relevant data models to improve resilience, to invest in big data research to catch up to other industries and to address data sharing challenges and big data analytics skills gaps in the electric industry.

The report discusses three main areas where work is needed. The first is existing DOE big data efforts, which would benefit from greater awareness of grid analytics, automating and sharing analytics as well as supporting the energy resilience model. That model will enable the simulation and tracking of electronic phenomena, faults and oscillations as they happen in real time. For DOE to use it, however, “the model will need to incorporate all data that the federal government is engineering and using in the weapons labs and by the U.S. Department of Defense,” the report states.

To address these issues, the report recommends that DOE use physical and data models, fund data analytics testbeds where apps can be demonstrated and evaluated and facilitate collaboration among utilities, vendors and government organizations.

The second area is around data, data availability and data requirements. Issues within this space include data ownership and privacy, cybersecurity and putting data in context.

“EAC and broader electric industry sector is interested in DOE’s role in data curation and hosting,” the report adds. “DOE could create requirements for data quality and promote standards for interoperability along with communication network capacity requirements. DOE has developed several data repositories, including with synthetic datasets. The differentiation of the value of synthetic networks for certain data analytics studies needs to be made.”

For privacy and confidentiality, the department could, for example, use customer metadata to analyze energy efficiency, the report suggests. Another idea: DOE should find out what databases the government owns and maintains that can be used in developing utility data analytics applications and then have those databases offer preprocessed data for automated analytics support.

The third area covers data analytics, use cases and cost recovery. It calls out the existing architecture for supporting utility monitoring and control and protection functions as several decades old and states that most utilities do not have an analytics department.

EAC’s recommendations for this area include workforce development through partnerships with universities, learning to anticipate future needs for control systems and coordinating with state and federal agencies to address cost models for future data analytics.

“The greatest way to add value may be to cross-pollinate traditional utility and other sectors’ data analytics capabilities, such as the broader sectors of the Internet of Things, fintech, and others that employ data analytical techniques,” the report states.

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Data is the basis of innovation. It’s the main reason why businesses collect pertinent information from consumers, as they could translate to powerful business predictions and decisions. But at the rate companies across the globe is accumulating mounds of raw data, there’s never enough qualified professionals to manage the torrential downpour of information. It’s no surprise data scientists are incredibly in demand, as they have the capacity to transform data into quantifiable pieces of information that can then turn into fact-based decisions.

If you want to capitalize on this demand, there’s no better time than now to harness and sharpen your data analytics and visualization skills. The Dynamic Data Scientist Bundle Ft. Power BI, Python & MATLAB was specifically put together to help you on that front, and for a limited time, you can get it on sale for only $29.99.

Regardless of your experience with organizing, processing, and analyzing data, this 7-part e-learning bundle will impart with you the skills to help make accurate and powerful data-driven predictions for any organization. With instructional content led by experts, you’ll learn straight from top data science specialists things like coding a multitude of data structures, mathematical concepts used in machine learning, creating visualizations and altering them to accommodate various situations, and effective data gathering and visualization techniques to make sense of raw information.

In addition to all these, the bundle also offers courses on using the different tools that data scientists have in their arsenal. You’ll be guided through what Microsoft Power Business Intelligence (BI) is and how it can be leveraged to view and share insights into businesses. You’ll discover MATLAB to figure out how machine learning works and get familiarized with Python to make line and scatter plots, as well as create various types of visualizations.

Future-proof your career and make yourself indispensable by learning data science. The bundle usually retails for $1,393, but for a limited time, you can get it on sale for only $29.99.

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San Francisco: The increasing use of artificial intelligence is going to extract a heavy price in power unless the chip industry steps up and heads that off, according to one of the industry’s biggest companies.

Data centers are on course to consume 15% of the world’s electricity by 2025, according to Applied Materials Inc., the world’s largest maker of chip equipment. Those giant warehouses of computers currently suck in about 2%, the company said.

“AI has the potential to change everything,” said Applied Materials Chief Executive Officer Gary Dickerson speaking in a prerecorded remote keynote for the industry’s Semicon West conference. “But AI has an Achilles’ heel that — unless addressed — will prevent it from reaching its true potential. That Achilles heel is power consumption. Training neural networks is incredibly energy-intensive when done with the technology that’s available today.”

A flood of new devices are getting internet connections, generating more data and increasing the need for more computing power with artificial intelligence to make sense of that new information. Chipmakers, most of which use Applied’s machinery, have made their electronic components more energy efficient but not enough, according to Dickerson. The industry needs to come up with new custom designs tailored for AI processing and new ways of connecting those chips, he said.

Widths on the tiny circuits that give chips their functions are measured in billionths of a meter. But to move and store data quickly they require large amounts of power. An Intel Corp. Xeon processor may draw more than 200 watts, as much as an old-fashioned portable tube TV. Put thousands of those processors in close proximity and couple them with all of the other components needed to make a server and the electricity drain adds up.

Applied’s CEO made a promise to reduce the company’s power footprint and to innovate on the basics of manufacturing and materials to help his customers make more efficient components. One example he gave Tuesday involves a technique for growing tungsten atoms in a vacuum to make more efficient connections between parts of the chip.

Applied will move to 100% renewable energy and cut its carbon footprint by 50% over the next 10 years, Dickerson pledged. Even relatively simple changes in practices can help, he said. Flying an engineer from the U.S. to Asia and back generates about 2 metric tons of carbon dioxide. Applied has learned during the Covid-19 pandemic how to improve remote research and development and customer support, he said, making fewer such trips necessary.

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Attitudes about nuclear energy are changing, with pundits on both sides of the aisle touting its benefits for extremely efficient and relatively clean energy. Despite an ever more positive public opinion, the nuclear industry in the United States, the largest in the world, is currently experiencing a downturn, even going so far as to need government subsidies to keep afloat.

In fact, at present the fastest growing sector of the nuclear industry is profiting not off of growth, but off of the nuclear sector’s slow death in the United States. According to reporting by Bloomberg, “the fastest growing part of the nuclear industry in the U.S. involves a small but expanding group of companies that specialize in tearing reactors down faster and cheaper than ever before.” this statement begins the article appropriately entitled “Fastest-Growing Nuclear Business Is Tearing Down U.S. Plants“.

Tearing down old nuclear reactors is no easy feat, however. Not only is it historically extremely expensive, it’s also highly hazardous. Even in nuclear plants in good condition, it’s a job that requires the utmost level of care and a ton of specialized gear in order to protect workers from radioactive materials. “Those who do handle radioactive material must first don protective suits that are inherently cumbersome and are further encumbered by the air hoses needed to allow the wearer to breathe,” a report from the Economist details. “Even then their working hours are strictly limited, in order to avoid prolonged exposure to radiation and because operating in the suits is exhausting. Moreover, some sorts of waste are too hazardous for even the besuied to approach safely.”

And then there are reactors that have experienced a recent accident or meltdown–they need cleanup more than any other, but who should be the workers who have to risk their own health for the health of the masses? According to some forward-thinking scientists and other experts in the field, there is a clear and humanitarian answer to this question. Robots.

Some may remember that this idea is not a totally new one, and a robot was sent into the Fukushima nuclear power plant in Japan shortly after an earthquake-related nuclear disaster took place there in 2011. Some remotely operated robots have already become a standard fixture in the decommissioning of nuclear facilities, but the machines widely in use are not yet sophisticated enough to easily and efficiently do the complex tasks necessary to clean up a nuclear reactor. One team at Lancaster University has been working on a new, semi-autonomous robot that would be able to perform the kind of actions that the current robots can’t, making nuclear cleanup an even easier and less dangerous job.

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What do a Yemeni refugee in the queue for food aid, a checkout worker in a British supermarket and a depressed university student have in common? They’re all being sifted by some form of artificial intelligence.

Advanced nations and the world’s biggest companies have thrown billions of dollars behind AI – a set of computing practices, including machine learning that collate masses of our data, analyse it, and use it to predict what we would do.

Yet cycles of hype and despair are inseparable from the history of AI. Is that clunky robot really about to take my job? How do the non-geeks among us distinguish AI’s promise from the hot air and decide where to focus concern?

Computer scientist Jaron Lanier ought to know. An inventor of virtual reality, Lanier worked with AI pioneer Marvin Minsky, one of the people who coined the term “artificial intelligence” in the 1950s. Lanier insists AI, then and now, is mostly a marketing term. In our interview, he recalled years of debate with Minsky about whether AI was real or a myth:

“At one point, [Minsky] said to me, ‘Look, whatever you think about this, just play along, because it gets us funding, this’ll be great.’ And it’s true, you know … in those days, the military was the principal source of funding for computer science research. And if you went into the funders and you said, ‘We’re going to make these machines smarter than people some day and whoever isn’t on that ride is going to get left behind and big time. So we have to stay ahead on this, and boy! You got funding like crazy.'”

But at worst, he says, AI can be more insidious: a ploy the powerful use to shirk responsibility for the decisions they make. If “computer says, ‘no,'” as the old joke goes, to whom do you complain?

We’d all better find out quickly. Whether or not you agree with Lanier about the term AI, machine learning is getting more sophisticated, and it’s in use by everyone from the tech giants of Silicon Valley to cash-strapped local authorities. From credit to jobs to policing to healthcare, we’re ceding more and more power to algorithms, or rather – to the people behind them.

Many applications of AI are incredible: we could it to improve wind farms or spot cancersooner. But that isn’t the only, or even the main, AI trend. The worrying ones involve the assessment and prediction of people – and, in particular, grading for various kinds of risk.

As a human rights lawyer doing “war on terror” cases, I thought a lot about our attitudes to risk. Remember Vice President Dick Cheney’s “one percent doctrine”? He said that any risk – even one percent – of a terror attack would, in the post-9/11 world, to be treated like a certainty.

That was just a complex way of saying that the US would use force based on the barest suspicion about a person. This attitude survived the transition to a new administration – and the shift to a machine learning-driven process in national security, too.

During President Barack Obama’s drone wars, suspicion didn’t even need to be personal – in a “signature strike”, it could be a nameless profile, generated by an algorithm, analysing where you went and who you talked to on your mobile phone. This was made clear in an unforgettable comment by ex-CIA and NSA director Michael Hayden: “We kill people based on metadata,” he said.

Now a similar logic pervades the modern marketplace, the sense that total certainty and zero risk – that is, zero risk for the class of people Lanier describes as “closest to the biggest computer” – is achievable and desirable. This is what is crucial for us all to understand: AI isn’t just about Google and Facebook targeting you with advertisements. It’s about risk.

The police in Los Angeles believed it was possible to use machine learning to predict crime. London’s Metropolitan Police, and others, want to use it to see your face wherever you go. Credit agencies and insurers want to build a better profile to understand whether you might get heart disease, or drop out of work, or fall behind on payments.

It used to be common to talk about “the digital divide”. This originally meant that the skills and advantages of connected citizens in rich nations would massively outrun poorer citizens without computers and the Internet. The solution: get everyone online and connected. This drove policies like One Laptop Per Child – and it drives newer ones, like Digital ID, the aim to give everyone on Earth a unique identity, in the name of economic participation. And connectivity has, at times, indeed opened people to new ideas and opportunities.

But it also comes at a cost. Today, a new digital divide is opening. One between the knowers and the known. The data miners and optimisers, who optimise, of course, according to their values, and the optimised. The surveillance capitalists, who have the tools and the skills to know more about everyone, all the time, and the world’s citizens.

AI has ushered in a new pecking order, largely set by our proximity to this new computational power. This should be our real concern: how advanced computing could be used to preserve power and privilege.

This is not a dignified future. People are right to be suspicious of this use of AI, and to seek ways to democratise this technology. I use an iPhone and enjoy, on this expensive device, considerably less personalised tracking of me by default than a poorer user of an Android phone.

When I apply for a job in law or journalism, a panel of humans interviews me; not an AI using “expression analysis” as I would experience applying for a job in a Tesco supermarket in the UK. We can do better than to split society into those who can affordprivacy and personal human assessment – and everyone else, who gets number-crunched, tagged, and sorted.

Unless we head off what Shoshana Zuboff calls “the substitution of computation for politics” – where decisions are taken outside of a democratic contest, in the grey zone of prediction, scoring, and automation – we risk losing control over our values.

The future of artificial intelligence belongs to us all. The values that get encoded into AI ought to be a matter for public debate and, yes, regulation. Just as we banned certain kinds of discrimination, should certain inferences by AI be taken off the table? Should AI firms have a statutory duty to allow in auditors to test for bias and inequality?

Is a certain platform size (say, Facebook and Google, which drive much AI development now and supply services to over two billion people) just too big – like Big Rail, Big Steel, and Big Oil of the past? Do we need to break up Big Tech?

Everyone has a stake in these questions. Friendly panels and hand-picked corporate “AI ethics boards” won’t cut it. Only by opening up these systems to critical, independent enquiry – and increasing the power of everyone to participate in them – will we build a just future for all.

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