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AI Weekly: AI researchers release toolkit to promote AI that helps to achieve sustainability goals

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While discussions about AI often center around the technology’s commercial potential, increasingly, researchers are investigating ways that AI can be harnessed to drive societal change. Among others, Facebook chief AI scientist Yann LeCun and Google Brain cofounder Andrew Ng have argued that mitigating climate change and promoting energy efficiency are preeminent challenges for AI researchers.

Along this vein, researchers at the Montreal AI Ethics Institute have proposed a framework designed to quantify the social impact of AI through techniques like compute-efficient machine learning. An IBM project delivers farm cultivation recommendations from digital farm “twins” that simulate the future soil conditions of real-world crops. Other researchers are using AI-generated images to help visualize climate change, and nonprofits like WattTime are working to reduce households’ carbon footprint by automating when electric vehicles, thermostats, and appliances are active based on where renewable energy is available.

Seeking to spur further explorations in the field, a group at the Stanford Sustainability and Artificial Intelligence Lab this week released (to coincide with NeurIPS 2021) a benchmark dataset called SustainBench for monitoring sustainable development goals (SDGs) including agriculture, health, and education using machine learning. As the coauthors told VentureBeat in an interview, the goal is threefold: (1) lower the barriers to entry for researchers to contribute to achieving SDGs; (2) provide metrics for evaluating SDG-tracking algorithms, and (3) encourage the development of methods where improved AI model performance facilitates progress towards SDGs.

“SustainBench was a natural outcome of the many research projects that [we’ve] worked on over the past half-decade. The driving force behind these research projects was always the lack of large, high-quality labeled datasets for measuring progress toward the United Nations Sustainable Development Goals (UN SDGs), which forced us to come up with creative machine learning techniques to overcome the label sparsity,” the coauthors said. “[H]aving accumulated enough experience working with datasets from diverse sustainability domains, we realized earlier this year that we were well-positioned to share our expertise on the data side of the machine learning equation … Indeed, we are not aware of any prior sustainability-focused datasets with similar size and scale of SustainBench.”

Motivation

Progress toward SDGs has historically been measured through civil registrations, population-based surveys, and government-orchestrated censuses. However, data collection is expensive, leading many countries to go decades between taking measurements on SDG indicators. It’s estimated that only half of SDG indicators have regular data from more than half of the world’s countries, limiting the ability of the international community to track progress toward the SDGs.

“For example, early on during the COVID-19 pandemic, many developing countries implemented their own cash transfer programs, similar to the direct cash payments from the IRS in the United States. However … data records on household wealth and income in developing countries are often unreliable or unavailable,” the coauthors said.

Innovations in AI have shown promise in helping to plug the data gaps, however. Data from satellite imagery, social media posts, and smartphones can be used to train models to predict things like poverty, annual land cover, deforestation, agricultural cropping patterns, crop yields, and even the location and impact of natural disasters. For example, the governments of Bangladesh, Mozambique, Nigeria, Togo, and Uganda used machine learning-based poverty and cropland maps to direct economic aid to their most vulnerable populations during the pandemic.

But progress has been hindered by challenges, including a lack of expertise and dearth of data for low-income countries. With SustainBench, the Stanford researchers — along with contributors at Caltech, UC Berkeley, and Carnegie Mellon — hope to provide a starting ground for training machine learning models that can help measure SDG indicators and have a wide range of applications for real-world tasks.

SustainBench contains a suite of 15 benchmark tasks across seven SDGs taken from the United Nations, including good health and well-being, quality education, and clean water and sanitation. Beyond this, SustainBench offers tasks for machine learning challenges that cover 119 countries, each designed to promote the development of SDG measurement methods on real-world data.

The coauthors caution that AI-based approaches should supplement, rather than replace, ground-based data collection. They point out that ground truth data are necessary for training models in the first place, and that even the best sensor data can only capture some — but not all — of the outcomes of interest. But AI, they still believe, can be helpful for measuring sustainability indicators in regions where ground truth measurements are scarce or unavailable.

“[SDG] indicators have tremendous implications for policymakers, yet ‘key data are scarce, and often scarcest in places where they are most needed,’ as several of our team members wrote in a recent Science review article. By using abundant, cheap, and frequently updated sensor data as inputs, AI can help plug these data gaps. Such input data sources include publicly available satellite images, crowdsourced street-level images, Wikipedia entries, and mobile phone records, among others,” the coauthors said.

Future work

In the short term, the coauthors say that they’re focused on raising awareness of SustainBench within the machine learning community. Future versions of SustainBench are in the planning stages, potentially with additional datasets and AI benchmarks.

“Two technical challenges stand out to us. The first challenge is to develop machine learning models that can reason about multi-modal data. Most AI models today tend to work with single data modalities (e.g., only satellite images, or only text), but sensor data often comes in many forms … The second challenge is to design models that can take advantage of the large amount of unlabeled sensor data, compared to sparse ground truth labels,” the coauthors said. “On the non-technical side, we also see a challenge in getting the broader machine learning community to focus more efforts on sustainability applications … As we alluded to earlier, we hope SustainBench makes it easier for machine learning researchers to recognize the role and challenges of machine learning for sustainability applications.”

For AI coverage, send news tips to Kyle Wiggers — and be sure to subscribe to the AI Weekly newsletter and bookmark our AI channel, The Machine.

Thanks for reading,

Kyle Wiggers

AI Staff Writer

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The FTC files suit to block Microsoft’s Activision Blizzard acquisition

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The Federal Trade Commission is suing to block the proposed acquisition of Activision Blizzard by Microsoft. It contends that the acquisition, if completed, would give Microsoft an unfair advantage over its competitors.

This morning, the four-person commission voted to issue the lawsuit. The three Democrat members (chair Lina Khan, Rebecca Slaughter and Alvaro Bedoya) voted in favor and the Republican (Christine Wilson) voted against. The commission allegedly met with Microsoft the day prior to discuss concessions, according to a report from The Washington Post.

If its news release is anything to go by, the commissioners weren’t convinced that Microsoft wouldn’t withhold Activision Blizzard’s popular games from competing services. The FTC cited Microsoft’s acquisition of Zenimax, and how games such as Starfield and Redfall became exclusive following its close.

Holly Vedova, director of the FTC’s Bureau of Competition, said in a statement, “Microsoft has already shown that it can and will withhold content from its gaming rivals. Today we seek to stop Microsoft from gaining control over a leading independent game studio and using it to harm competition in multiple dynamic and fast-growing gaming markets.”

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The FTC is not the only government body to express concern about the implications of the acquisition. The UK’s Competition and Markets Authority is currently investigating. It recently closed Phase One of the investigation, and expressed concerns in its issues statement.

The history of the planned acquisition

Microsoft announced its intention to acquire the publisher in January. Through this acquisition, it would become the regent of popular gaming franchises such as Call of Duty, Candy Crush, World of Warcraft and many others. Reportedly, it offered around $69 billion for Activision Blizzard.

The concerns about the scale of the acquisition emerged almost as soon as it was announced. The FTC reportedly moved to investigate the deal almost immediately. Niko Partners senior analyst Daniel Ahmad said at the time that Microsoft would have to pay Activision $3 billion if the deal was blocked.

The current focal point of the antitrust concerns is the Call of Duty franchise. Sony has repeatedly contended, in public statements primarily aimed at the CMA’s investigation, that Microsoft could undermine its competition via these popular and lucrative games. It could, according to Sony, either outright stop publishing them on Sony’s platforms, or it could offer them on its Xbox Game Pass subscription service. Sony claims Call of Duty on Game Pass would diminish demand for Sony consoles even if Call of Duty is still published on them.

Microsoft has, in turn, responded that its competitors have plenty of exclusive titles of their own. It’s also offered to sign 10-year deals with Sony, Nintendo and Valve (the company behind PC games store Steam) to offer Call of Duty titles on their platforms. It announced earlier this week that it has inked such a deal with Nintendo.

Brad Smith, Microsoft’s vice chair and president, said in a statement to The Verge, “We continue to believe that this deal will expand competition and create more opportunities for gamers and game developers. We have been committed since Day One to addressing competition concerns, including by offering earlier this week proposed concessions to the FTC. While we believed in giving peace a chance, we have complete confidence in our case and welcome the opportunity to present our case in court.”



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Airtable chief revenue officer, chief people officer and chief product officer are out • TechCrunch

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As part of Airtable’s decision to cut 20% of staff, or 254 employees, three executives are “parting ways” with the company as well, a spokesperson confirmed over email. The chief revenue officer, chief people officer and chief product officer are no longer with the company.

Airtable’s chief revenue officer, Seth Shaw, joined in November 2020 just one month before Airtable’s chief producer officer Peter Deng came on board. Airtable’s chief people officer, Johanna Jackman, joined Airtable in May 2021 with an ambitious goal to double the company’s headcount to 1,000 in 12 months. The three executives are departing today as a mutual decision with Airtable, but will advise the company through the next phase of transition, the company says. All three executives were reached out to for further comment and this story will be updated with their responses if given.

An Airtable spokesperson declined to comment on if the executives were offered severance pay. The positions will be succeeded by internal employees, introduced at an all-hands meeting to be held this Friday.

Executive departures at this scale are rare, even if the overall company is going through a heavy round of cuts. But CEO and founder Howie Liu emphasized, in an email sent to staff but seen by TechCrunch, that the decision – Airtable’s first-ever lay off in its decade-long history – was made following Airtable’s choice to pivot to a more “narrowly focused mode of execution.”

In the email, Liu described Airtable’s goal – first unveiled in October – to capture enterprise clients with connected apps. Now, instead of the bottom-up adoption that first fueled Airtable’s rise, the company wants to be more focused in this new direction. Liu’s e-mail indicates that the startup will devote a majority of its resources toward “landing and expanding large enterprise companies with at least 1k FTEs – where our connected apps vision will deliver the most differentiated value.”

The lean mindset comes after Airtable reduced spend in marketing media, real estate, business technology and infrastructure, the e-mail indicates. “In trying to do too many things at once, we have grown our organization at a breakneck pace over the past few years. We will continue to emphasize growth, but do so by investing heavily in the levers that yield the highest growth relative to their cost,” Liu wrote.

Airtable seems to be emphasizing that its reduced spend doesn’t come with less ambition, or ability to execute. A spokesperson added over e-mail that all of Airtable’s funds from its $735 million Series F are “still intact.” They also said that the startup’s enterprise side, which makes up the majority of Airtable’s revenue, is growing more than 100% year over year; the product move today just doubles down on that exact cohort.

Current and former Airtable employees can reach out to Natasha Mascarenhas on Signal, a secure encrypted messaging app, at 925 271 0912. You can also DM her on Twitter, @nmasc_. 



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Kubernetes Gateway API reality check: Ingress controller is still needed

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No doubt the new Kubernetes excitement is the Gateway API. One of the more significant changes in the Kubernetes project, the Gateway API is sorely needed. More granular and robust control over Kubernetes service networking better addresses the growing number of use cases and roles within the cloud-native paradigm.

Shared architecture — at all scales — requires flexible, scalable and extensible means to manage, observe and secure that infrastructure. The Gateway API is designed for those tasks. Once fully matured, it will help developers, SREs, platform teams, architects and CTOs by making Kubernetes infrastructure tooling and governance more modular and less bespoke.

But let’s be sure the hype does not get ahead of today’s needs.

The past and future Kubernetes gateway API

There remains a gap between present and future states of Ingress control in Kubernetes. This has led to a common misconception that the Gateway API will replace the Kubernetes Ingress Controller (KIC) in the near term or make it less useful over the longer term. This view is incorrect for multiple reasons.

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Ingress controllers are now embedded in the functional architecture of most Kubernetes deployments. They have become de facto. At some point, the Gateway API will be sufficiently mature to replace all functionality of the Ingress API and even the implementation-specific annotations and custom resources that many of the Ingress implementations use, but that day remains far off.

Today, most IT organizations are still either in the early adoption or the testing stage with Kubernetes. For many, just getting comfortable with the new architecture, networking constructs, and application and service management requirements requires considerable internal education and digestion.

Gateway API and Ingress controllers are not mutually exclusive

As we’ve done at NGINX, other Ingress maintainers will presumably implement the Gateway API in their products to take advantage of the new functionality and stay current with the Kubernetes API and project. Just as RESTful APIs are useful for many tasks, the Kubernetes API underpins many products and services, all built on the foundation of its powerful container orchestration engine.

The Gateway API is designed to be a universal component layer for managing service connectivity and behaviors within Kubernetes. It is expressive and extensible, making it useful for many roles, from DevOps to security to NetOps.

As a team that has invested considerable resources into an open source Ingress controller, NGINX could have chosen to integrate the Gateway API into our existing work. Instead, we elected to leverage the Gateway API as a standalone, more open-ended project. We chose this path so as not to project the existing constraints of our Ingress controller implementation onto ways we might hope to use the Gateway API or NGINX in the future. With fewer constraints, it is easier to fail faster or to explore new designs and concepts. Like most cloud-native technology, the Gateway API construct is designed for loose coupling and modularity ­— even more so than the Ingress controller, in fact.

We are also hopeful that some of our new work around the Gateway API is taken back into the open-source community. We have been present in the Kubernetes community for quite some time and are increasing our open-source efforts around the Gateway API.

It could be interpreted that the evolving API provides an invaluable insertion point and opportunity for a “do-over” on service networking. But that does not mean that everyone is quick to toss out years of investment in other projects. Ingress will continue to be important as Gateway API matures and develops, and the two are not mutually exclusive.

Plan for a hybrid future

Does it sound like we think the Kubernetes world should have its Gateway API cake and eat its Ingress controller too? Well, we do. Guilty as charged. Bottom line: We believe Kubernetes is a big tent with plenty of room for both new constructs and older categories. Improving on existing Ingress controllers —which were tethered to a limited annotation capability that induced complexity and reduced modularity — remains critical for organizations for the foreseeable future.

Yes, the Gateway API will help us improve Ingress controllers and unleash innovation, but it’s an API, not a product category. This new API is not a magic wand nor a silver bullet. Smart teams are planning for this hybrid future, learning about the improvements the Gateway API will bring while continuing to plan around ongoing Ingress controller improvement. The beauty of this hybrid reality is that everyone can run clusters in the way they know and desire. Every team gets what they want and need.

Brian Ehlert is director of product management at NGINX.

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