Our Nature paper describes AlphaFold, a system that generates3D models of proteins that are far more accurate than any that have come before.Read More
Artificial Intelligence, Values and Alignment
This paper looks at philosophical questions that arise in the context of AI alignment. It defends three propositions. First, normative and technical aspects of the AI alignment problem are interrelated, creating space for productive engagement between people working in both domains. Second, it is important to be clear about the goal of alignment. There are significant differences between AI that aligns with instructions, intentions, revealed preferences, ideal preferences, interests and values. A principle-based approach to AI alignment, which combines these elements in a systematic way, has considerable advantages in this context. Third, the central challenge for theorists is not to identify ‘true’ moral principles for AI; rather, it is to identify fair principles for alignment, that receive reflective endorsement despite widespread variation in people’s moral beliefs. The final part of the paper explores three ways in which fair principles for AI alignment could potentially be identified.Read More
International evaluation of an AI system for breast cancer screening
Screening mammography aims to identify breast cancer before symptoms appear, enabling earlier therapy for more treatable disease. Despite the existence of screening programs worldwide, interpretation of these images suffers from suboptimal rates of false positives and false negatives. Here we present an AI system capable of surpassing a single expert reader in breast cancer prediction performance.Read More
Using WaveNet technology to reunite speech-impaired users with their original voices
We demonstrate an early proof of concept of how text-to-speech technologies can synthesise a high-quality, natural sounding voice using minimal recorded speech data.Read More
Learning human objectives by evaluating hypothetical behaviours
We present a new method for training reinforcement learning agents from human feedback in the presence of unknown unsafe states.Read More
From unlikely start-up to major scientific organisation: Entering our tenth year at DeepMind
Weve come a long way in building the organisation we need to achieve our long-term mission.Read More
Strengthening the AI community
AI requires people with different experiences, knowledge and backgrounds, which is why we started the DeepMind Scholarship programme and supportuniversitiesand the wider ecosystem.Read More
Advanced machine learning helps Play Store users discover personalised apps
In collaboration with Google Play,our team that leads on collaborations with Googlehas driven significant improvements in the Play Store’s discovery systems, helping to deliver a more personalised and intuitive Play Store experience for users.Read More
AlphaStar: Grandmaster level in StarCraft II using multi-agent reinforcement learning
AlphaStar is the first AI to reach the top league of a widely popular esport without any game restrictions.Read More
Causal Bayesian Networks: A flexible tool to enable fairer machine learning
Decisions based on machine learning (ML) are potentially advantageous over human decisions, but the data used to train them often contains human and societal biases that can lead to harmful decisions.Read More