Open-access multispectral imagery from missions like Landsat 8-9 and Sentinel-2 has fueled the development of geospatial foundation models (GFMs) for humanitarian and environmental applications. Yet, their deployment remains limited by (i) the absence of automated geospatial data pipelines and (ii) the large size of fine-tuned models. Existing GFMs lack workflows for processing raw satellite imagery, and downstream adaptations often retain the full complexity of the original encoder. We present InstaGeo, an open-source, end-to-end framework that addresses these challenges by integrating: (1) automated data curation to transform raw imagery into model-ready datasets; (2) task-specific model distillation to derive compact, compute-efficient models; and (3) seamless deployment as interactive web-map applications. Using InstaGeo, we reproduced datasets from three published studies and trained models with marginal mIoU differences of -0.73 pp for flood mapping, -0.20 pp for crop segmentation, and +1.79 pp for desert locust prediction. The distilled models are up to 8x smaller than standard fine-tuned counterparts, reducing FLOPs and CO2 emissions with minimal accuracy loss. Leveraging InstaGeo's streamlined data pipeline, we also curated a larger crop segmentation dataset, achieving a state-of-the-art mIoU of 60.65%, a 12 pp improvement over prior baselines. Moreover, InstaGeo enables users to progress from raw data to model deployment within a single working day. By unifying data preparation, model compression, and deployment, InstaGeo transforms research-grade GFMs into practical, low-carbon tools for real-time, large-scale Earth observation. This approach shifts geospatial AI toward data quality and application-driven innovation. Source code, datasets, and model checkpoints are available at: https://github.com/instadeepai/InstaGeo-E2E-Geospatial-ML.git

Offline multi-agent reinforcement learning (MARL) is an emerging field with great promise for real-world applications. Unfortunately, the current state of research in offline MARL is plagued by inconsistencies in baselines and evaluation protocols, which ultimately makes it difficult to accurately assess progress, trust newly proposed innovations, and allow researchers to easily build upon prior work. In this paper, we firstly identify significant shortcomings in existing methodologies for measuring the performance of novel algorithms through a representative study of published offline MARL work. Secondly, by directly comparing to this prior work, we demonstrate that simple, well-implemented baselines can achieve state-of-the-art (SOTA) results across a wide range of tasks. Specifically, we show that on 35 out of 47 datasets used in prior work (almost 75% of cases), we match or surpass the performance of the current purported SOTA. Strikingly, our baselines often substantially outperform these more sophisticated algorithms. Finally, we correct for the shortcomings highlighted from this prior work by introducing a straightforward standardised methodology for evaluation and by providing our baseline implementations with statistically robust results across several scenarios, useful for comparisons in future work. Our proposal includes simple and sensible steps that are easy to adopt, which in combination with solid baselines and comparative results, could substantially improve the overall rigour of empirical science in offline MARL moving forward.

Welcome to the latest issue of your guide to AI, an editorialized newsletter covering key developments in AI research, industry, geopolitics and startups during January 2023. This one is a monster so it might get clipped in your inbox (read the online version in case!). Nathan wrote an oped in The Times for why university spinouts are a critical engine for our technology industry and why spinout policy needs urgent reform. The Times Higher Education profiled our open source data term database, spinout.fyi. Nathan commented on The Financial Times' Big Read on The growing tensions around spinouts at British universities. The State of AI Report provided two key figures to The Economist's piece on The race of the AI labs heats up. Register for next year's RAAIS, a full-day event in London that explores research frontiers and real-world applications of AI-first technology at the world's best companies. As usual, we love hearing what you're up to and what's on your mind, just hit reply or forward to your friends:-) BioNTech acquired London and Tunis-based AI startup InstaDeep for $680M (cash stock) - this was a huge deal.

BioNTech has announced a planned acquisition of London-based InstaDeep in a deal that will see £362 million in cash and BioNTech shares, excluding the shares already owned by BioNTech with InstaDeep shareholders eligible to receive additional performance-based future milestone payments up to approximately £200 million. The move will allow the German biopharma company to rapidly incorporate a number of Instadeep's validated and novel BioNTech-trained AI- and ML-based models across BioNTech's discovery platforms and connected, through InstaDeep's DeepChain platform, to an integrated automated lab infrastructure. Ultimately, this means that BioNTech, which was focusing on cancer treatments prior to shooting to stardom through its partnership with pfizer, developing the omnipresent COVID-19 vaccine, can now develop more, faster, and perhaps even better than ever before. The acquisition is more of a formality at this point, as the two companies have a track record that extends back nearly four years now. In November 2020, the companies announced a collaboration and joint AI Innovation Lab that has been aimed at applying the latest advances in AI and ML technology to develop novel medicines for a range of cancers and infectious diseases.

Jan 10 (Reuters) - BioNTech SE has agreed to acquire British artificial intelligence (AI) startup InstaDeep for up to 562 million pounds ($682 million) to speed up its biotech research and manufacturing capabilities. Under the German vaccine maker's largest takeover deal to date, BioNTech is to pay 362 million pounds upfront, in a mix of cash and an unspecified number of BioNTech shares, and up to 200 million pounds contingent on InstaDeep's future performance. "Our goal with the acquisition is to integrate AI seamlessly in all aspects of our work - from target discovery, lead discovery to manufacturing and delivery of our products," BioNTech co-founder and Chief Executive Ugur Sahin said at the J.P. Morgan healthcare conference on Tuesday. Sahin also cited BioNTech's partnership last week with the U.K. government for development of personalized cancer therapies and how AI would help in that. The transaction adds to a slew of deals as the industry meets in San Francisco for the conference this week.

InstaDeep is an EMEA leader in delivering decision-making AI products. Leveraging their extensive know-how in GPU-accelerated computing, deep learning, and reinforcement learning, they have built products, such as the novel DeepChain platform, to tackle the most complex challenges across a range of industries. InstaDeep has also developed collaborations with global leaders in the AI ecosystem, such as Google DeepMind, NVIDIA, and Intel. They are part of Intel's AI Builders program and are one of only 2 NVIDIA Elite Service Delivery Partners across EMEA. The InstaDeep team is made up of approximately 155 people working across its network of offices in London, Paris, Tunis, Lagos, Dubai, and Cape Town, and is growing fast.

InstaDeep, one of the leaders in the design of decision-making Artificial Intelligence systems, announced on January 25 that it had raised $100 million (€88 million). The company closed a Series B round led by DeepTech investment firm Alpha Intelligence Capital and supported by CDIB. BioNTech, Chimera Abu Dhabi, Deutsche Bahn Digital Ventures, Google, G42 and Synergie participated in this latest round. Founded in 2014 by Karim Beguir and Zohra Slim, InstaDeep is a leader in decision AI systems, it has been named two years in a row to the CB Insights AI 100 ranking of the world's 100 most promising private artificial intelligence companies. The company develops patented AI products such as its DeepChainTM protein design platform and collaborates with leading companies such as Google DeepMind, Nvidia and Intel.

AI has the potential to generate meaningful returns for the enterprise. Responding to a 2018 PricewaterhouseCoopers survey, 54% of business executives say that their adoption of AI within the workplace has led to a boost in productivity. A separate 2019 McKinsey report found that 44% of firms using AI achieved a reduction in business costs in departments where AI is implemented. But barriers stand in the way of deployment, including a lack of production-grade data and expensive tools and development processes. Among the top challenges enterprises face in adopting AI is an absence of in-house talent.

Today we are joined by Karim Beguir, Co-Founder and CEO of InstaDeep. InstaDeep, based in Tunisia, Africa, is focused on building advanced decision-making systems for the enterprise. Karim's goal is to show that advanced AI and Deep Learning is taking place in Africa, solving real-world problems and building a new generation of talent in the AI industry. With offices around the world, InstaDeep works with large companies in multiple industries with this episode focusing on logistical challenges, like ride-sharing and container shipping. These problems require decision-making in complex environments with a large number of choices.

Adversarial self-play in two-player games has delivered impressive results when used with reinforcement learning algorithms that combine deep neural networks and tree search. Algorithms like AlphaZero and Expert Iteration learn tabula-rasa, producing highly informative training data on the fly. However, the self-play training strategy is not directly applicable to single-player games. Recently, several practically important combinatorial optimization problems, such as the traveling salesman problem and the bin packing problem, have been reformulated as reinforcement learning problems, increasing the importance of enabling the benefits of self-play beyond two-player games. We present the Ranked Reward (R2) algorithm which accomplishes this by ranking the rewards obtained by a single agent over multiple games to create a relative performance metric. Results from applying the R2 algorithm to instances of a two-dimensional bin packing problem show that it outperforms generic Monte Carlo tree search, heuristic algorithms and reinforcement learning algorithms not using ranked rewards.