That project, known as the Oman Australia Cable, was spearheaded by SUBCO, a Brisbane-based subsea cable investment company owned by Australian entrepreneur Bevan Slattery.

“Silicon Valley is waking up to the reality that it has to pick a side,”

Microsoft – whose President Brad Smith said in 2017 that the tech sector needed to be a “neutral digital Switzerland” – announced in May that it had discovered Chinese state-sponsored hackers targeting U.S. critical infrastructure, a rare example of a big tech firm calling out Beijing for espionage.

America’s SubCom, Japan’s NEC Corporation, France’s Alcatel Submarine Networks and China’s HMN Tech.

First, Washington needs SubCom to expand the Navy’s undersea cable network so that it can better coordinate military operations and enhance surveillance on China’s expanding fleet of submarines and warships, the people said. Second, the Biden administration wants SubCom to build more commercial subsea internet cables controlled by U.S. companies, a strategy aimed at ensuring that America remains the primary custodian of the internet, according to the two industry officials.

Subsea cables are vulnerable to sabotage and espionage, and Beijing and Washington have accused each other of tapping cables to spy on data or carry out cyberattacks.

SubCom’s journey from Cold War experiment to global cable constructor and now a shadowy player in the U.S.-China tech war is detailed in this story for the first time.

This dual role has made SubCom increasingly valuable to Washington as global internet infrastructure – from undersea cables to data centers and 5G mobile networks – risks fracturing into two systems, one backed by the United States, the other controlled by China.

SubCom is the exclusive undersea cable contractor to the U.S. military, laying a web of internet and surveillance cables across the ocean floor

The CS Dependable is owned by SubCom, a small-town New Jersey cable manufacturer that’s playing an outsized role in a race between the United States and China to control advanced military and digital technologies that could decide which country emerges as the world’s preeminent superpower.

The long-term vision of enabling any employee — and customers as well — to easily access important knowledge within and outside of a company to enhance productivity and innovation is a powerful draw. Generative AI appears to be the technology that is finally making it possible.

Any company that commits to embedding its own knowledge into a generative AI system should be prepared to revise its approach to the issue frequently over the next several years.

either though training or policies — include:

Generative AI capabilities, including awareness of context and history, generating new content by aggregating or combining knowledge from various sources, and data-driven predictions, can provide powerful support for knowledge work.

To realize opportunities and manage potential risks of generative AI applications to knowledge management, companies need to develop a culture of transparency and accountability that would make generative AI-based knowledge management systems successful.

User prompts into publicly-available LLMs are used to train future versions of the system, so some companies (Samsung, for example) have feared propagation of confidential and private information and banned LLM use by employees. However, most companies’ efforts to tune LLMs with domain-specific content are performed on private instances of the models that are not accessible to public users, so this should not be a problem. In addition, some generative AI systems such as ChatGPT allow users to turn off the collection of chat histories, which can address confidentiality issues even on public systems.

the company’s attorneys helped create a series of “pre-prompts” that tell the generative AI system what types of questions it should answer and those it should politely avoid.

many legal observers feel that “fair use” provisions of copyright law will apply to them, although this hasn’t been tested in the courts (and not all countries have such provisions in their copyright laws).

For example, for BloombergGPT, which is intended for answering financial and investing questions, the system was evaluated on public dataset financial tasks, named entity recognition, sentiment analysis ability, and a set of reasoning and general natural language processing tasks.

The good news is that companies who have tuned their LLMs on domain-specific information have found that hallucinations are less of a problem than out-of-the-box LLMs, at least if there are no extended dialogues or non-business prompts.

if content authors are aware of how to create effective documents.

Most companies that do not have well-curated content will find it challenging to do so for just this purpose.

Morgan Stanley, for example, has a group of 20 or so knowledge managers in the Philippines who are constantly scoring documents along multiple criteria; these determine the suitability for incorporation into the GPT-4 system.

The alternative is to create vector embeddings — arrays of numeric values produced from the text by another pre-trained machine learning model (Morgan Stanley uses one from OpenAI called Ada).

The prompt-trained system is operated in a private cloud that is only accessible to Morgan Stanley employees.

Morgan Stanley, for example, used prompt tuning to train OpenAI’s GPT-4 model using a carefully curated set of 100,000 documents with important investing, general business, and investment process knowledge.

It requires considerable data science expertise; the scientific paper for the Google project, for example, had 31 co-authors.

with Google’s general PaLM2 LLM

to add specific domain content to a system that is already trained on general knowledge and language-based interaction.

Bloomberg’s data scientists employed 700 tokens, or about 350 billion words, 50 billion parameters, and 1.3 million hours of graphics processing unit time.

some of the same factors that made knowledge management difficult in the past are still present.

the heyday of the “knowledge management” movement in the 1990s and early 2000s, but most companies found the technology of the time inadequate for the task

a generative AI-based system for customer support led to increased productivity of customer support agents and improved retention, while leading to higher positive feedback on the part of customers.

can’t respond to prompts or questions regarding proprietary content or knowledge.

to express complex ideas in articulate language

knowledge within organizations is typically generated and captured across various sources and forms, including individual minds, processes, policies, reports, operational transactions, discussion boards, and online chats and meetings.

through effective and agile creation, management, application, recombination, and deployment of knowledge assets and know-how.

We have to be very, very careful about ensuring that it doesn’t come across as AI surveillance,

introducing AI ethicists

While there are laws being whipped up around how employers should ethically and responsibly implement AI around, for example, hiring, so as to ensure job posts aren’t discriminatory in any way (like NYC’s Law 144), there are still relatively few guardrails.

more traditional kind, which detects patterns from data and provides predictive analysis, which has been used for the last few years by some businesses, but isn’t yet mainstream.

they need to invest in reskilling their workforces

A bit like the customer relationship management software that airlines use, to create a more personalized travel experience.

“I’m a big believer in human interaction and connection. I don’t think that will go away. Automation and speed will help us do different things, focusing on more strategic, creative endeavors. But it will still be on us to understand our people on a human level.”

The theory goes, that if people are saving time on more administrative, tedious aspects of their work, they’ll have more brain capacity for creative and strategic thinking.

improve the meaning and purpose of work.

“Knowing that AI will change many roles in the workforce, CEOs should encourage people to embrace experimentation with the technology and communicate the upskilling opportunities for them to work in tandem with the AI, rather than be replaced by it,”

“AI is not a technology that can be given to a CTO or a similar position, it is a tool that must be understood by the CEO and the entire management team.”

four “trust-building” processes: improve the governance of AI systems and processes; confirm AI-driven decisions are interpretable and easily explainable; monitor and report on AI model performance and protect AI systems from cyber threats and manipulations, per a 2023 Trust survey from management consultancy PwC.

to road test AI.

but leaders still need to be able to frame the issues, make the smart calls and ensure they’re well executed.”

the human element of interpreting data and asking AI the right questions to make sound judgments, will be crucial.

Most (94%) business leaders agree that AI is critical for success, per a 2022 Deloitte report.

we must not dismiss legitimate concerns regarding the alignment problem

We should prioritize targeted regulation for specific applications — recognizing that each domain comes with its own set of ethical dilemmas and policy challenges.

shift in the allocation of National Science Foundation (NSF) funding toward responsible AI research

addressing data privacy reform is essential.

we implement retention and reproducibility requirements for AI research

the government must limit abuse across applications using existing laws, such as those governing data privacy and discrimination

Proposals for centralized government-backed projects underestimate the sheer diversity of opinions among AI researchers.

while the Manhattan Project’s ultimate goal was relatively singular — design and build the atomic bomb — AI safety encompasses numerous ambiguities ranging from the meaning of concepts like “mechanistic interpretability” to “value alignment.

to address the “alignment problem,” the fear that powerful AI models might not act in a way we ask of them; or to address mechanistic interpretability, the ability to understand the function of each neuron in a neural network.

gets mired in unprecedented alarmism rather than focusing on addressing these more proximate — and much more likely — challenges.

rogue actors using large AI models to dismantle cybersecurity around critical infrastructure; political parties using disinformation at scale to destabilize fragile democratic governments; domestic terrorists using these models to learn how to build homemade weapons; and dictatorial regimes using them to surveil their populations or build dystopian social credit systems, among others.

distracts from the grounded conversations necessary to develop well-informed policies around AI governance.

The technology is neither inherently good nor evil; in contrast, philosophers, ethicists, and even the pope have argued that the same could not necessarily be said about nuclear weapons, because their mere possession is an inherent threat to kill millions of people.

These types of common exaggerations ultimately detract from effective policymaking aimed at addressing both immediate risks and potential catastrophic threats posed by certain AI technologies.

Bundling occurs when a company offers multiple products together as a single package.

Experience has also shown how firms can use “open first, closed later” tactics in ways that undermine long-term competition.

The open-source innovation explosion in image generation, coupled with new developments in optimizations, made it possible for nearly anyone to develop, iterate on, and deploy the models using smaller datasets and lower-cost consumer hardware

Additionally, some markets for specialized chips are—or could be, without appropriate competition policies and antitrust enforcement—highly concentrated.

the fine-tuning technique LoRa (Low-Rank Adaptation) can enable a developer to fine-tune a model to perform a specific task using consumer grade GPUs

Access to computational resources is a third key input in generative AI markets.

Companies that can acquire both the engineering experience and professional talent necessary to build and package the final generative AI product or service will be better positioned to gain market share.

Developing a generative model requires a significant engineering and research workforce with particular—and relatively rare—skillsets, as well as a deep understanding of machine learning, natural language processing, and computer vision.

This may be particularly true in specialized domains or domains where data is more highly regulated, such as healthcare or finance

First, more established companies may benefit from access to data collected from their users over many years—especially if the incumbents also own digital platforms that amass large amounts of data.

The volume and quality of data required to pre-train a generative AI model from scratch may impact the ability of new players to enter the market.

The pre-training step creates a base model with broad competency in a specific domain, such as language or images.

t is especially important that firms not engage in unfair methods of competition or other antitrust violations to squash competition and undermine the potential far-reaching benefits of this transformative technology.

AI has proved to be a controversial topic in education resulting in questions over authenticity. The Russell Group, which includes Cambridge University, has set out principles for its use

What principles should guide AI when handling topics that involve both human rights and local cultural or legal differences, like LGBTQ rights and women’s rights? Should AI responses change based on the location or culture in which it’s used?

"...experiments in setting up a democratic process for deciding what rules AI systems should follow, within the bounds defined by the law."

This fall I will be using it in my courses for the first time. As an example of what I intend, I am going to make some written assignments a contest for students to come up with the best essay that has been generated by AI. This will force them to engage with AI, enter a number of prompts, and then evaluate the output with specific grading rubrics and their own knowledge of the subject.

linear regression.

Just fyi this is known as “pointed ai”… or predictive analytics.

By posing a series of related questions, the model develops a coherent chain of thought, leading to more coherent and contextually appropriate responses.

By encouraging structured reasoning, the model can generate more reliable and accurate responses.

By leveraging such comprehensive textual resources, language models can exhibit a broader understanding of various topics.

"Mandating direct payments to telecom operators in the EU absent assurances on spending could reinforce the dominant market position of the largest operators,"

And so it makes me feel diminished. It makes me feel, in some sense, like a very imperfect, flawed structure compared with these computational systems that have, you know, a million times or a billion times more knowledge than I have and are a billion times faster. It makes me feel extremely inferior.

he defeat of Gary Kasparov by Deep Blue, and then going on to Go systems, Go programs, well, systems that could defeat some of the best Go players in the world. And then systems got better and better at translation between languages, and then at producing intelligible responses to difficult questions in natural language, and even writing poetry.

Ambassador George Mina of Australia encouraged members to emphasize the significance of the initiative on e-commerce with trade ministers and to ensure they are engaged not only in the economic aspects of it but also in the larger strategic significance of the initiative.

“While e-commerce has helped small businesses, and especially women-owned firms, tap into international markets, there is a great deal of room to improve. Digital trade can be an even more effective economic lifeline for marginalized groups and geographically remote areas.”

o date, the initiative has “parked” 11 provisions, including paperless trading, e-contracts, e-signatures, e-invoicing, spam, consumer protection, cybersecurity and an electronic transactions framework. The negotiators hope to bridge differences on other provisions, such as “single windows”, personal information and data protection, by the summer.

But if the ocean nodules can be brought to market affordably, the sheer volume of metal available may start to ease the pressure on Indonesian forests.

There are other environmental arguments in favour of mining the seabed. The nodules contain much higher concentrations of metal than deposits on land, which means less energy is required to process them. Peter Tom Jones, the director of the KU Leuven Institute for Sustainable Metals and Materials, in Belgium, reckons that processing the nodules will produce about 40% less greenhouse-gas emissions than those from terrestrial ore.

Around 13 kilograms of biomass would be lost for every tonne of CCZ nickel mined.

the island nation of Nauru

Over the past five years most of the growth in demand has been met by Indonesia, which has been bulldozing rainforests to get at the ore beneath. In 2017 the country produced just 17% of the world’s nickel, according to CRU , a metals research firm. Today it is responsible for around half, or 1.6m tonnes a year, and that number is rising. CRU thinks Indonesia will account for 85% of production growth between now and 2027. Even so, that is unlikely to be enough to meet rising demand. And as Indonesian nickel production increases, it is expected to replace palm-oil production as the primary cause of deforestation in the country.

Nickel in particular is in short supply.

Demand for the minerals from which those batteries are made is soaring.

A proposed mandate from Trudeau’s office says the group would issue reports aimed at guiding the development of policies that could keep AI technology grounded in human rights. It lists several areas of interest, including how data for AI projects is collected and accessed, the effect of AI on human rights, and whether people trust AI technology. The group will also discuss military uses of AI.

The celebrated psycholinguist argues that “the curse of knowledge” is the biggest cause of bad writing: like children, writers forget that others often do not know what they know.

Orwell exhorted writers to “never use the passive where you can use the active”, Williams explains how passives can sometimes help create a sense of flow.

But if you can first grasp the origins and qualities of bad writing, you may learn to diagnose and cure problems in your own prose (keeping things simple helps a lot).

Britain becoming an AI superpower.

As the US expands the number of critical data sets that are publicly available, their conditions of use and access will become increasingly important (Bates 2014).

“It appears there was some sort of confrontation between the two men in the house and the Israeli army, and the two men in the house were shot dead,”

Some regulations intervene in the market while others facilitate the market; some regulations benefit large companies and others harm them.

Both Democrats and Republicans support regulations that would require companies to label AI creations as such,

As a self-professed pro-union president, Biden must answer to unions across the country that worry that the technology could eliminate workers’ jobs.

By racing to regulate AI, lawmakers could miss the opportunity to address some of the technology’s less obvious dangers. Machine learning algorithms are often biased, explained Eric Rice, who founded USC’s Center for AI in Society. Several years ago, researchers found that a popular healthcare risk-prediction algorithm was racially biased, with Black patients receiving lower risk scores.

Schaake expressed concern that when Altman and others warn of existential threats from AI, they are putting the regulatory focus on the horizon, rather than in the present. If lawmakers are worrying about AI ending humanity, they’re overlooking the more immediate, less dramatic worries.

AI companies should adhere to “an appropriate set of safety requirements,” which could entail a government-run licensing or registration system.

AI governance is a journey, not a destination.

The concept of safety brakes, along with licensing for highly capable foundation models and AI infrastructure obligations, should be key elements of the voluntary, internationally coordinated G7 code that signatory nation states agree to incorporate into their national systems.

a means for mutual recognition of compliance and safety across borders

against internationally agreed standards

the OECD to develop principles for trustworthy AI

an AI system certified as safe in one jurisdiction can also qualify as safe in another.

a voluntary AI Code of Conduct.

s well as India and Indonesia

In each area, the key to success will be to develop concrete initiatives and bring governments, companies, and NGOs together to advance them.

Unless academic researchers can obtain access to substantially more computing resources, there is a real risk that scientific and technological inquiry will suffer, including that relating to AI itself.

Transparency requirements in the AI Act, and potentially several of the to-be-developed standards related to the Act, present an opportunity to leverage such industry initiatives towards a shared goal.

One of these is the Coalition for Content Provenance Authenticity, or C2PA, a global standards body with more than 60 members including Adobe, the BBC, Intel, Microsoft, Publicis Groupe, Sony, and Truepic. The group is dedicated to bolstering trust and transparency of online information including releasing the world’s first technical specification for certifying digital content in 2022, which now includes support for Generative AI.

whenever an AI system is used to create artificially generated content, this should be easy to identify.

The AI Act will require that AI providers make it clear to users that they are interacting with an AI system.

The AI Act requires developers of high-risk systems to put in place a risk management system to ensure that systems are tested, to mitigate risks to the extent possible, including through responsible design and development, and to engage in post-market monitoring.

t’s also important to make sure that obligations are attached to powerful AI models, with a focus on a defined class of highly capable foundation models and calibrated to model-level risk. This will impact two layers of the technology stack. The first will require new regulations for these models themselves. And the second will involve obligations for the AI infrastructure operators on which these models are developed and deployed. The blueprint we developed offers suggested goals and approaches for each of these layers.

The AI Act acknowledges the challenges to regulating complex architecture through its risk-based approach for establishing requirements for high-risk systems.

They would be akin to the braking systems engineers have long built into other technologies such as elevators, school buses, and high-speed trains, to safely manage not just everyday scenarios, but emergencies as well.

Our blueprint proposes new safety requirements that, in effect, would create safety brakes for AI systems that control the operation of designated critical infrastructure.

As the EU finalizes the AI Act, the EU could consider using procurement rules to promote the use of relevant trustworthy AI frameworks. For instance, when procuring high-risk AI systems, EU procurement authorities could require suppliers to certify via third-party audits that they comply with relevant international standards.

such as the AI Risk Management Framework developed by the U.S. National Institute of Standards and Technology, or NIST, and the new international standard ISO/IEC 42001 on AI Management Systems, which is expected to be published in the fall of 2023.

we’ve been supportive of a regulatory regime in Europe that effectively addresses safety and upholds fundamental rights while continuing to enable innovations that will ensure that Europe remains globally competitive.

People who design and operate AI systems cannot be accountable unless their decisions and actions are subject to the rule of law.

This is the fundamental need to ensure that machines remain subject to effective oversight by people, and the people who design and operate machines remain accountable to everyone else.

accountability

There are enormous opportunities to harness the power of AI to contribute to European growth and values. But another dimension is equally clear. It’s not enough to focus only on the many opportunities to use AI to improve people’s lives. We need to focus with equal determination on the challenges and risks that AI can create, and we need to manage them effectively.

the Algorithmic Accountability Act.

the banning of certain applications of AI, like sentiment analysis or facial recognition, echoing parts of the EU regulation

it would require that tech companies identify and label AI-made text and images, which is a massive undertaking.

should tech companies have that same ‘get out of jail free’ pass for AI-generated content?

A giant unanswered question for AI regulation in the US is whether we will or won’t see Section 230 reform.

I must reflect “communist values.”)

The subtext here is the narrative that US AI companies are different from Chinese AI companies.

Technology, and AI in particular, ought to be aligned with “democratic values.”

regulations from the European Union, which some tech companies and critics say will stifle innovation.

regulators will probably be calling on tech CEOs to ask how they’d like to be regulated.

Schumer called innovation the “north star” of US AI strategy

Individual agencies like the FTC,the Department of Commerce, and the US Copyright Office have been quick to respond to the craze of the last six months, issuing policy statements, guidelines, and warnings about generative AI in particular.

a National AI Commission to manage AI policy,

a bill that would exclude generative AI from Section 230 (the law that shields online platforms from liability for the content their users create).

emain a high-stakes cat-and-mouse game

AI has become a double-edged sword in the era of digital information.

the potential of AI in safeguarding truth and trust in the digital age is vast.

may generate false positives and negatives,

AI models need large volumes of labeled data for training, but there's a shortage of labeled deepfake datasets.

by analyzing the linguistic structure of the content, cross-referencing it with verified databases, tracking the origin and propagation patterns, and more.

to realistically simulate blinking

AI models are being trained to spot deepfakes by identifying subtle patterns that humans might miss.

The generator improves its fakes based on feedback from the discriminator, resulting in extremely realistic deepfakes.

Generative Adversarial Networks (GANs)

Deepfakes, powered by advanced machine learning techniques, can create hyper-realistic videos or audio where individuals appear to say or do things that they never did

some form of “guidance or policy” before the US presidential elections next year, and hopes the industry can “converge on a small set of high-level principles.”

He also said regulation could create a more level playing field for companies that are already trying to follow safe and ethical best practices.

plans to “actively engage with governments and all stakeholders to establish risk-based regulatory frameworks” around AI.

pointed to the CAI’s “nutritional label” format, which gives viewers a sense of AI involvement in creation while not revealing too much about a given creative professional’s “secret sauce.”

the Content Authenticity Initiative (CAI) aimed at bringing more transparency to AI-generated content, said they hope that their efforts will help the group stay ahead of any potential regulations.

We have all kinds of use cases for AI, and it has to expand beyond GPT and large language models, or what we’re gonna legislate is GPT and large language models—not AI,

“Maybe there should be some other method to make sure that people are acting well—have some scoring system,

‘Please give licenses to make big models,’” he said. “And if you see this from the perspective of trying to use regulation as a way to close the door behind you, all of the testimony fits in perfectly.”

Senators on both sides of the aisle generally agreed on the need for regulation

Microsoft President Brad Smith also threw the company’s support behind the creation of a new government agency and licensing system for pre-trained AI models at an event in Washington

Altman, along with IBM’s Christina Montgomery, made suggestions for how Congress should take action, with Altman proposing “licensing and testing requirements” and Montgomery suggesting “precision regulation” of specific use cases.

Search Console recommendation:

New Predictive Insights

aving a complete guide to the behavior and interactions of your active users.

If Universal Analytics was all about page views, GA4 is all about events.

to collect web and app data

Changes to online privacy policies for one, and changes in consumer behavior for another.

Man is born free; and everywhere he is in chains.

In the fu­ture, the new mod­u­la­tion format they de­veloped is likely to in­crease band­widths in other data trans­mis­sion meth­ods where the en­ergy of the beam can be­come a lim­it­ing factor.

French space com­pany, Thales Alenia Space is an ex­pert in tar­get­ing lasers with cen­ti­metre ac­cur­acy over thou­sands of kilo­metres in space. ONERA, also French, is an aerospace re­search in­sti­tute with ex­pert­ise in MEMS-​based ad­apt­ive op­tics, which has largely elim­in­ated the ef­fects of shim­mer­ing in the air. The most ef­fect­ive method of sig­nal mod­u­la­tion, which is es­sen­tial for high data rates, is a spe­cialty of Leuthold’s ETH Zurich re­search group.

Paris-​based pro­ject part­ner ONERA de­ployed a mi­cro­elec­tromech­an­ical sys­tem (MEMS) chip with a mat­rix of 97 tiny ad­justable mir­rors. The mir­rors’ de­form­a­tions cor­rect the phase shift of the beam on its in­ter­sec­tion sur­face along the cur­rently meas­ured gradi­ent 1,500 times per second, ul­ti­mately im­prov­ing the sig­nals by a factor of about 500.

Laser op­tical sys­tems, in con­trast, op­er­ate in the near-​infrared range with wavelengths of a few mi­cro­metres, which are about 10,000 times shorter. As a res­ult, they can trans­port more in­form­a­tion per unit of time.

How­ever, trans­mit­ting data between satel­lites and ground sta­tions uses ra­dio tech­no­lo­gies, which are con­sid­er­ably less power­ful. Like a wire­less local area net­work (WLAN) or mo­bile com­mu­nic­a­tions, such tech­no­lo­gies op­er­ate in the mi­crowave range of the spec­trum and thus have wavelengths meas­ur­ing sev­eral cen­ti­metres.

The laser beam travels through the dense at­mo­sphere near the ground. In the pro­cess, many factors – di­verse tur­bu­lence in the air over the high snow-​covered moun­tains, the wa­ter sur­face of Lake Thun, the densely built-​up Thun met­ro­pol­itan area and the Aare plane – in­flu­ence the move­ment of the light waves and con­sequently also the trans­mis­sion of data. The shim­mer­ing of the air, triggered by thermal phe­nom­ena, dis­turbs the uni­form move­ment of light and can be seen on hot sum­mer days by the na­ked eye.

“For op­tical data trans­mis­sion, our test route between the High Alti­tude Re­search Sta­tion on the Jung­frau­joch and the Zi­m­mer­wald Ob­ser­vat­ory at the Uni­ver­sity of Bern is much more chal­len­ging than between a satel­lite and a ground sta­tion,”

National defence is a paradigmatic example of a public good.

goods are usually defined as public goods if and only if they are both non-rivalrous and non-excludable (e.g., Varian 1992: 414)

Bob’s consumption of a grain of rice makes it impossible for Sally to consume the same grain of rice. By contrast, Sally’s enjoyment of Bruckner’s Symphony No. 9 in no way diminishes Bob’s ability to do the same. Rice is thus rivalrous while music is not.

he experience gained through the engagement of the EU Office in San Francisco with the tech sectorshould be used

the establishment of informaldigital hubs

The Council calls forthe informal EU Digital Diplomacy Networkto continue to engagein strategic discussions on key emerging and challenging issues of tech and digitalpolicyand regularly to convene in enlarged format, bringing in, as appropriate, other European and like-minded partners, as well as other stakeholdersand relevant networks, andto further strengthen its coordination with the EU Cyber Ambassadors’ Network

the Council calls on the High Representative, the Commission, and Member Statesto enhance digital capacity building and cooperation with Africa

The Digital for Development (D4D) Hubis a good example of the Team Europe approach to digital cooperation with partner regions globally.