2023: A Year of Groundbreaking Advances in AI and Computing

This has been a yr of unbelievable progress within the area of Synthetic Intelligence (AI) analysis and its sensible functions.

As ongoing analysis pushes AI even farther, we glance again to our perspective printed in January of this yr, titled “Why we concentrate on AI (and to what finish),” the place we famous:

We’re dedicated to main and setting the usual in creating and delivery helpful and helpful functions, making use of moral ideas grounded in human values, and evolving our approaches as we be taught from analysis, expertise, customers, and the broader neighborhood.

We additionally imagine that getting AI proper — which to us entails innovating and delivering extensively accessible advantages to folks and society, whereas mitigating its dangers — have to be a collective effort involving us and others, together with researchers, builders, customers (people, companies, and different organizations), governments, regulators, and residents.

We’re satisfied that the AI-enabled improvements we’re centered on creating and delivering boldly and responsibly are helpful, compelling, and have the potential to help and enhance lives of individuals in all places — that is what compels us.

On this Yr-in-Assessment submit we’ll go over a few of Google Analysis’s and Google DeepMind’s efforts placing these paragraphs into observe safely all through 2023.

Advances in Merchandise & Applied sciences

This was the yr generative AI captured the world’s consideration, creating imagery, music, tales, and fascinating dialog about every part possible, at a stage of creativity and a velocity nearly implausible a couple of years in the past.

In February, we first launched Bard, a device that you should use to discover inventive concepts and clarify issues merely. It will possibly generate textual content, translate languages, write totally different sorts of inventive content material and extra.

In Might, we watched the outcomes of months and years of our foundational and utilized work introduced on stage at Google I/O. Principally, this included PaLM 2, a big language mannequin (LLM) that introduced collectively compute-optimal scaling, an improved dataset combination, and mannequin structure to excel at superior reasoning duties.

By fine-tuning and instruction-tuning PaLM 2 for various functions, we had been in a position to combine it into quite a few Google merchandise and options, together with:

• An replace to Bard, which enabled multilingual capabilities. Since its preliminary launch, Bard is now accessible in additional than 40 languages and over 230 countries and territories, and with extensions, Bard can discover and present related info from Google instruments used on daily basis — like Gmail, Google Maps, YouTube, and extra.
• Search Generative Experience (SGE), which makes use of LLMs to reimagine each easy methods to set up info and easy methods to assist folks navigate by means of it, making a extra fluid, conversational interplay mannequin for our core Search product. This work prolonged the search engine expertise from primarily centered on info retrieval into one thing rather more — able to retrieval, synthesis, inventive era and continuation of earlier searches — whereas persevering with to function a connection level between customers and the net content material they search.
• MusicLM, a text-to-music mannequin powered by AudioLM and MuLAN, which may make music from textual content, buzzing, photos or video and musical accompaniments to singing.
• Duet AI, our AI-powered collaborator that gives customers with help after they use Google Workspace and Google Cloud. Duet AI in Google Workspace, for instance, helps customers write, create photos, analyze spreadsheets, draft and summarize emails and chat messages, and summarize conferences. Duet AI in Google Cloud helps customers code, deploy, scale, and monitor functions, in addition to determine and speed up decision of cybersecurity threats.
• And plenty of other developments.

In June, following final yr’s launch of our text-to-image era mannequin Imagen, we launched Imagen Editor, which gives the power to make use of area masks and pure language prompts to interactively edit generative photos to offer rather more exact management over the mannequin output.

Later within the yr, we launched Imagen 2, which improved outputs through a specialised picture aesthetics mannequin primarily based on human preferences for qualities such pretty much as good lighting, framing, publicity, and sharpness.

In October, we launched a characteristic that helps people practice speaking and improve their language skills. The important thing know-how that enabled this performance was a novel deep studying mannequin developed in collaboration with the Google Translate staff, referred to as Deep Aligner. This single new mannequin has led to dramatic enhancements in alignment high quality throughout all examined language pairs, decreasing common alignment error fee from 25% to five% in comparison with alignment approaches primarily based on Hidden Markov models (HMMs).

In November, in partnership with YouTube, we introduced Lyria, our most superior AI music era mannequin thus far. We launched two experiments designed to open a brand new playground for creativity, DreamTrack and music AI instruments, in live performance with YouTube’s Principles for partnering with the music industry on AI technology.

Then in December, we launched Gemini, our most succesful and basic AI mannequin. Gemini was constructed to be multimodal from the bottom up throughout textual content, audio, picture and movies.

Our preliminary household of Gemini fashions is available in three totally different sizes, Nano, Professional, and Extremely. Nano fashions are our smallest and best fashions for powering on-device experiences in merchandise like Pixel. The Professional mannequin is highly-capable and greatest for scaling throughout a variety of duties. The Extremely mannequin is our largest and most succesful mannequin for extremely advanced duties.

In a technical report about Gemini models, we confirmed that Gemini Extremely’s efficiency exceeds present state-of-the-art outcomes on 30 of the 32 widely-used tutorial benchmarks utilized in LLM analysis and improvement. With a rating of 90.04%, Gemini Extremely was the primary mannequin to outperform human specialists on MMLU, and achieved a state-of-the-art rating of 59.4% on the brand new MMMU benchmark.

Constructing on AlphaCode, the primary AI system to carry out on the stage of the median competitor in aggressive programming, we introduced AlphaCode 2 powered by a specialised model of Gemini. When evaluated on the identical platform as the unique AlphaCode, we discovered that AlphaCode 2 solved 1.7x extra issues, and carried out higher than 85% of competitors members

On the identical time, Bard got its biggest upgrade with its use of the Gemini Professional mannequin, making it much more succesful at issues like understanding, summarizing, reasoning, coding, and planning. In six out of eight benchmarks, Gemini Professional outperformed GPT-3.5, together with in MMLU, one of many key requirements for measuring giant AI fashions, and GSM8K, which measures grade college math reasoning. Gemini Extremely will come to Bard early subsequent yr by means of Bard Superior, a brand new cutting-edge AI expertise.

Gemini Professional can also be accessible on Vertex AI, Google Cloud’s end-to-end AI platform that empowers builders to construct functions that may course of info throughout textual content, code, photos, and video. Gemini Pro was also made available in AI Studio in December.

To greatest illustrate a few of Gemini’s capabilities, we produced a series of short videos with explanations of how Gemini may:

ML/AI Analysis

Along with our advances in merchandise and applied sciences, we’ve additionally made a variety of essential developments within the broader fields of machine studying and AI analysis.

On the coronary heart of probably the most superior ML fashions is the Transformer mannequin structure, developed by Google researchers in 2017. Initially developed for language, it has confirmed helpful in domains as various as computer vision, audio, genomics, protein folding, and extra. This yr, our work on scaling vision transformers demonstrated state-of-the-art outcomes throughout all kinds of imaginative and prescient duties, and has additionally been helpful in constructing more capable robots.

Increasing the flexibility of fashions requires the power to carry out higher-level and multi-step reasoning. This yr, we approached this goal following a number of analysis tracks. For instance, algorithmic prompting is a brand new methodology that teaches language fashions reasoning by demonstrating a sequence of algorithmic steps, which the mannequin can then apply in new contexts. This method improves accuracy on one middle-school arithmetic benchmark from 25.9% to 61.1%.

Within the area of visible query answering, in a collaboration with UC Berkeley researchers, we confirmed how we may better answer complex visual questions (“Is the carriage to the precise of the horse?”) by combining a visible mannequin with a language mannequin skilled to reply visible questions by synthesizing a program to carry out multi-step reasoning.

We are actually utilizing a general model that understands many aspects of the software development life cycle to robotically generate code overview feedback, reply to code overview feedback, make performance-improving recommendations for items of code (by studying from previous such adjustments in different contexts), repair code in response to compilation errors, and extra.

In a multi-year analysis collaboration with the Google Maps staff, we had been in a position to scale inverse reinforcement studying and apply it to the world-scale problem of improving route suggestions for over 1 billion customers. Our work culminated in a 16–24% relative enchancment in international route match fee, serving to to make sure that routes are higher aligned with person preferences.

We additionally proceed to work on methods to enhance the inference efficiency of machine studying fashions. In work on computationally-friendly approaches to pruning connections in neural networks, we had been in a position to devise an approximation algorithm to the computationally intractable best-subset choice downside that is ready to prune 70% of the perimeters from a picture classification mannequin and nonetheless retain nearly the entire accuracy of the unique.

In work on accelerating on-device diffusion models, we had been additionally in a position to apply a wide range of optimizations to consideration mechanisms, convolutional kernels, and fusion of operations to make it sensible to run top quality picture era fashions on-device; for instance, enabling “a photorealistic and high-resolution picture of a cute pet with surrounding flowers” to be generated in simply 12 seconds on a smartphone.

Advances in succesful language and multimodal fashions have additionally benefited our robotics analysis efforts. We mixed individually skilled language, imaginative and prescient, and robotic management fashions into PaLM-E, an embodied multi-modal mannequin for robotics, and Robotic Transformer 2 (RT-2), a novel vision-language-action (VLA) mannequin that learns from each internet and robotics knowledge, and interprets this data into generalized directions for robotic management.

Moreover, we confirmed how language can also be used to control the gait of quadrupedal robots and explored the use of language to help formulate more explicit reward functions to bridge the hole between human language and robotic actions. Then, in Barkour we benchmarked the agility limits of quadrupedal robots.

Algorithms & Optimization

Designing environment friendly, strong, and scalable algorithms stays a excessive precedence. This yr, our work included: utilized and scalable algorithms, market algorithms, system effectivity and optimization, and privateness.

We launched AlphaDev, an AI system that makes use of reinforcement studying to find enhanced pc science algorithms. AlphaDev uncovered a sooner algorithm for sorting, a technique for ordering knowledge, which led to enhancements within the LLVM libc++ sorting library that had been as much as 70% sooner for shorter sequences and about 1.7% sooner for sequences exceeding 250,000 parts.

We developed a novel mannequin to predict the properties of large graphs, enabling estimation of efficiency for giant packages. We launched a brand new dataset, TPUGraphs, to speed up open research in this area, and confirmed how we will use modern ML to improve ML efficiency.

We developed a brand new load balancing algorithm for distributing queries to a server, referred to as Prequal, which minimizes a mix of requests-in-flight and estimates the latency. Deployments throughout a number of methods have saved CPU, latency, and RAM considerably. We additionally designed a brand new analysis framework for the classical caching downside with capability reservations.

We improved state-of-the-art in clustering and graph algorithms by creating new methods for computing minimum-cut, approximating correlation clustering, and massively parallel graph clustering. Moreover, we launched TeraHAC, a novel hierarchical clustering algorithm for trillion-edge graphs, designed a text clustering algorithm for higher scalability whereas sustaining high quality, and designed probably the most environment friendly algorithm for approximating the Chamfer Distance, the usual similarity perform for multi-embedding fashions, providing >50× speedups over highly-optimized actual algorithms and scaling to billions of factors.

We continued optimizing Google’s giant embedding fashions (LEMs), which energy a lot of our core merchandise and recommender methods. Some new methods embrace Unified Embedding for battle-tested characteristic representations in web-scale ML methods and Sequential Attention, which makes use of consideration mechanisms to find high-quality sparse mannequin architectures throughout coaching.

Past auto-bidding methods, we additionally studied public sale design in different advanced settings, similar to buy-many mechanisms, auctions for heterogeneous bidders, contract designs, and innovated robust online bidding algorithms. Motivated by the applying of generative AI in collaborative creation (e.g., joint advert for advertisers), we proposed a novel token auction model the place LLMs bid for affect within the collaborative AI creation. Lastly, we present easy methods to mitigate personalization effects in experimental design, which, for instance, could trigger suggestions to float over time.

The Chrome Privateness Sandbox, a multi-year collaboration between Google Analysis and Chrome, has publicly launched a number of APIs, together with for Protected Audience, Topics, and Attribution Reporting. It is a main step in defending person privateness whereas supporting the open and free internet ecosystem. These efforts have been facilitated by elementary analysis on re-identification risk, private streaming computation, optimization of privateness caps and budgets, hierarchical aggregation, and coaching fashions with label privacy.

Science and Society

Within the not too distant future, there’s a very actual chance that AI utilized to scientific issues can speed up the speed of discovery in sure domains by 10× or 100×, or extra, and result in main advances in various areas together with bioengineering, materials science, weather prediction, climate forecasting, neuroscience, genetic medicine, and healthcare.

Sustainability and Local weather Change

In Project Green Light, we partnered with 13 cities all over the world to assist enhance visitors stream at intersections and scale back stop-and-go emissions. Early numbers from these partnerships point out a possible for as much as 30% discount in stops and as much as 10% discount in emissions.

In our contrails work, we analyzed large-scale climate knowledge, historic satellite tv for pc photos, and previous flights. We trained an AI model to foretell the place contrails type and reroute airplanes accordingly. In partnership with American Airways and Breakthrough Vitality, we used this technique to display contrail discount by 54%.

We’re additionally creating novel technology-driven approaches to help communities with the effects of climate change. For instance, now we have expanded our flood forecasting coverage to 80 countries, which instantly impacts greater than 460 million folks. We have now initiated a number of research efforts to assist mitigate the growing hazard of wildfires, together with real-time tracking of wildfire boundaries utilizing satellite tv for pc imagery, and work that improves emergency evacuation plans for communities in danger to rapidly-spreading wildfires. Our partnership with American Forests places knowledge from our Tree Canopy challenge to work of their Tree Equity Score platform, serving to communities determine and deal with unequal entry to bushes.

Lastly, we continued to develop higher fashions for climate prediction at longer time horizons. Enhancing on MetNet and MetNet-2, on this yr’s work on MetNet-3, we now outperform conventional numerical climate simulations as much as twenty-four hours. Within the space of medium-term, international climate forecasting, our work on GraphCast confirmed considerably higher prediction accuracy for as much as 10 days in comparison with HRES, probably the most correct operational deterministic forecast, produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). In collaboration with ECMWF, we launched WeatherBench-2, a benchmark for evaluating the accuracy of climate forecasts in a typical framework.

Well being and the Life Sciences

The potential of AI to dramatically enhance processes in healthcare is important. Our preliminary Med-PaLM mannequin was the primary mannequin able to reaching a passing rating on the U.S. medical licensing examination. Our newer Med-PaLM 2 model improved by an extra 19%, reaching an expert-level accuracy of 86.5%. These Med-PaLM models are language-based, allow clinicians to ask questions and have a dialogue about advanced medical circumstances, and are available to healthcare organizations as a part of MedLM by means of Google Cloud.

In the identical means our basic language fashions are evolving to deal with a number of modalities, now we have just lately proven analysis on a multimodal version of Med-PaLM able to decoding medical photos, textual knowledge, and different modalities, describing a path for a way we will understand the thrilling potential of AI fashions to assist advance real-world medical care.

We have now additionally been engaged on how best to harness AI models in clinical workflows. We have now proven that coupling deep learning with interpretability methods can yield new insights for clinicians. We have now additionally proven that self-supervised studying, with cautious consideration of privateness, security, equity and ethics, can reduce the amount of de-identified data needed to coach clinically related medical imaging fashions by 3×–100×, decreasing the boundaries to adoption of fashions in actual medical settings. We additionally launched an open source mobile data collection platform for folks with power illness to offer instruments to the neighborhood to construct their very own research.

AI methods may uncover fully new alerts and biomarkers in present types of medical knowledge. In work on novel biomarkers discovered in retinal images, we demonstrated that a variety of systemic biomarkers spanning a number of organ methods (e.g., kidney, blood, liver) could be predicted from exterior eye photographs. In different work, we confirmed that combining retinal images and genomic information helps determine some underlying components of ageing.

Within the genomics house, we labored with 119 scientists throughout 60 establishments to create a new map of the human genome, or pangenome. This extra equitable pangenome higher represents the genomic range of worldwide populations. Constructing on our ground-breaking AlphaFold work, our work on AlphaMissense this yr gives a catalog of predictions for 89% of all 71 million doable missense variants as both seemingly pathogenic or seemingly benign.

We additionally shared an update on progress in direction of the subsequent era of AlphaFold. Our newest mannequin can now generate predictions for practically all molecules within the Protein Data Bank (PDB), often reaching atomic accuracy. This unlocks new understanding and considerably improves accuracy in a number of key biomolecule lessons, together with ligands (small molecules), proteins, nucleic acids (DNA and RNA), and people containing post-translational modifications (PTMs).

On the neuroscience entrance, we announced a new collaboration with Harvard, Princeton, the NIH, and others to map a whole mouse mind at synaptic decision, starting with a primary part that can concentrate on the hippocampal formation — the world of the mind chargeable for reminiscence formation, spatial navigation, and different essential features.

Quantum computing

Quantum computer systems have the potential to unravel huge, real-world issues throughout science and {industry}. However to understand that potential, they have to be considerably bigger than they’re at the moment, and so they should reliably carry out duties that can not be carried out on classical computer systems.

This yr, we took an essential step in direction of the event of a large-scale, helpful quantum pc. Our breakthrough is the primary demonstration of quantum error correction, displaying that it’s doable to scale back errors whereas additionally growing the variety of qubits. To allow real-world functions, these qubit constructing blocks should carry out extra reliably, decreasing the error fee from ~1 in 103 sometimes seen at the moment, to ~1 in 108.

Accountable AI Analysis

Design for Duty

Generative AI is having a transformative impression in a variety of fields together with healthcare, training, safety, vitality, transportation, manufacturing, and leisure. Given these advances, the significance of designing applied sciences according to our AI Principles stays a high precedence. We additionally just lately printed case research of emerging practices in society-centered AI. And in our annual AI Principles Progress Update, we provide particulars on how our Accountable AI analysis is built-in into merchandise and threat administration processes.

Proactive design for Accountable AI begins with figuring out and documenting potential harms. For instance, we just lately introduced a three-layered context-based framework for comprehensively evaluating the social and moral dangers of AI methods. Throughout mannequin design, harms could be mitigated with the usage of responsible datasets.

We’re partnering with Howard University to construct top quality African-American English (AAE) datasets to enhance our merchandise and make them work nicely for extra folks. Our analysis on globally inclusive cultural representation and our publication of the Monk Skin Tone scale furthers our commitments to equitable illustration of all folks. The insights we acquire and methods we develop not solely assist us enhance our personal fashions, in addition they energy large-scale studies of representation in popular media to tell and encourage extra inclusive content material creation all over the world.

With advances in generative picture fashions, fair and inclusive representation of people stays a high precedence. Within the improvement pipeline, we’re working to amplify underrepresented voices and to better integrate social context knowledge. We proactively deal with potential harms and bias utilizing classifiers and filters, careful dataset analysis, and in-model mitigations similar to fine-tuning, reasoning, few-shot prompting, data augmentation and controlled decoding, and our analysis confirmed that generative AI allows higher quality safety classifiers to be developed with far much less knowledge. We additionally launched a powerful way to better tune models with less data giving builders extra management of accountability challenges in generative AI.

We have now developed new state-of-the-art explainability methods to determine the position of coaching knowledge on mannequin behaviors. By combining training data attribution methods with agile classifiers, we discovered that we will determine mislabelled coaching examples. This makes it doable to scale back the noise in coaching knowledge, resulting in vital enhancements in mannequin accuracy.

We initiated a number of efforts to enhance security and transparency about on-line content material. For instance, we launched SynthID, a device for watermarking and figuring out AI-generated photos. SynthID is imperceptible to the human eye, would not compromise picture high quality, and permits the watermark to stay detectable, even after modifications like including filters, altering colours, and saving with numerous lossy compression schemes.

We additionally launched About This Image to assist folks assess the credibility of photos, displaying info like a picture’s historical past, the way it’s used on different pages, and accessible metadata about a picture. And we explored safety methods which were developed in different fields, studying from established conditions the place there’s low-risk tolerance.

Privateness stays a necessary facet of our dedication to Accountable AI. We continued bettering our state-of-the-art privateness preserving studying algorithm DP-FTRL, developed the DP-Alternating Minimization algorithm (DP-AM) to allow customized suggestions with rigorous privateness safety, and outlined a brand new general paradigm to scale back the privateness prices for a lot of aggregation and studying duties. We additionally proposed a scheme for auditing differentially private machine learning systems.

On the functions entrance we demonstrated that DP-SGD offers a practical solution within the giant mannequin fine-tuning regime and confirmed that photos generated by DP diffusion fashions are useful for a range of downstream tasks. We proposed a brand new algorithm for DP coaching of enormous embedding fashions that gives environment friendly coaching on TPUs with out compromising accuracy.

We additionally teamed up with a broad group of educational and industrial researchers to prepare the first Machine Unlearning Challenge to handle the state of affairs wherein coaching photos are forgotten to guard the privateness or rights of people. We shared a mechanism for extractable memorization, and participatory systems that give customers extra management over their delicate knowledge.

We continued to increase the world’s largest corpus of atypical speech recordings to >1M utterances in Project Euphonia, which enabled us to coach a Universal Speech Model to better recognize atypical speech by 37% on real-world benchmarks.

We additionally constructed an audiobook recommendation system for college kids with studying disabilities similar to dyslexia.

Adversarial Testing

Our work in adversarial testing engaged community voices from traditionally marginalized communities. We partnered with teams such because the Equitable AI Research Round Table (EARR) to make sure we signify the varied communities who use our fashions and engage with external users to determine potential harms in generative mannequin outputs.

We established a dedicated Google AI Red Team centered on testing AI fashions and merchandise for safety, privateness, and abuse dangers. We confirmed that assaults similar to “poisoning” or adversarial examples could be utilized to manufacturing fashions and floor extra dangers similar to memorization in each image and text generative models. We additionally demonstrated that defending towards such assaults could be difficult, as merely making use of defenses could cause different security and privacy leakages. We additionally launched mannequin analysis for extreme risks, similar to offensive cyber capabilities or sturdy manipulation abilities.

Democratizing AI Although Instruments and Schooling

As we advance the state-of-the-art in ML and AI, we additionally need to guarantee folks can perceive and apply AI to particular issues. We launched MakerSuite (now Google AI Studio), a web-based device that permits AI builders to shortly iterate and construct light-weight AI-powered apps. To assist AI engineers higher perceive and debug AI, we launched LIT 1.0, a state-of-the-art, open-source debugger for machine studying fashions.

Colab, our device that helps builders and college students entry highly effective computing sources proper of their internet browser, reached over 10 million customers. We’ve simply added AI-powered code assistance to all customers for gratis — making Colab an much more useful and built-in expertise in knowledge and ML workflows.

To make sure AI produces correct data when put to make use of, we additionally just lately launched FunSearch, a brand new method that generates verifiably true data in mathematical sciences utilizing evolutionary strategies and huge language fashions.

For AI engineers and product designers, we’re updating the People + AI Guidebook with generative AI greatest practices, and we proceed to design AI Explorables, which incorporates how and why models sometimes make incorrect predictions confidently.

Neighborhood Engagement

We proceed to advance the fields of AI and pc science by publishing a lot of our work and collaborating in and organizing conferences. We have now printed greater than 500 papers to date this yr, and have sturdy presences at conferences like ICML (see the Google Research and Google DeepMind posts), ICLR (Google Research, Google DeepMind), NeurIPS (Google Research, Google DeepMind), ICCV, CVPR, ACL, CHI, and Interspeech. We’re additionally working to help researchers all over the world, collaborating in occasions just like the Deep Learning Indaba, Khipu, supporting PhD Fellowships in Latin America, and extra. We additionally labored with companions from 33 tutorial labs to pool knowledge from 22 totally different robotic sorts and create the Open X-Embodiment dataset and RT-X model to raised advance accountable AI improvement.

Google has spearheaded an industry-wide effort to develop AI safety benchmarks beneath the MLCommons requirements group with participation from a number of main gamers within the generative AI house together with OpenAI, Anthropic, Microsoft, Meta, Hugging Face, and extra. Together with others within the {industry} we additionally co-founded the Frontier Model Forum (FMF), which is concentrated on guaranteeing protected and accountable improvement of frontier AI fashions. With our FMF companions and different philanthropic organizations, we launched a $10 million AI Safety Fund to advance analysis into the continuing improvement of the instruments for society to successfully check and consider probably the most succesful AI fashions.

In shut partnership with Google.org, we worked with the United Nations to construct the UN Data Commons for the Sustainable Development Goals, a device that tracks metrics throughout the 17 Sustainable Development Goals, and supported projects from NGOs, tutorial establishments, and social enterprises on using AI to accelerate progress on the SDGs.

The gadgets highlighted on this submit are a small fraction of the analysis work now we have finished all through the final yr. Discover out extra on the Google Research and Google DeepMind blogs, and our list of publications.

Future Imaginative and prescient

As multimodal fashions grow to be much more succesful, they’ll empower folks to make unbelievable progress in areas from science to training to completely new areas of data.

Progress continues apace, and because the yr advances, and our merchandise and analysis advance as nicely, folks will discover extra and fascinating inventive makes use of for AI.

Ending this Yr-in-Assessment the place we started, as we are saying in Why We Focus on AI (and to what end):

If pursued boldly and responsibly, we imagine that AI generally is a foundational know-how that transforms the lives of individuals in all places — that is what excites us!