DeepMind, a British company owned by Google, may be on the verge of achieving human-level artificial intelligence (AI). Nando de Freitas, a research scientist at DeepMind and machine learning professor at Oxford University, has said'the game is over' in regards to solving the hardest challenges in the race to achieve artificial general intelligence (AGI). AGI refers to a machine or program that has the ability to understand or learn any intellectual task that a human being can, and do so without training. According to De Freitas, the quest for scientists is now scaling up AI programs, such as with more data and computing power, to create an AGI. Earlier this week, DeepMind unveiled a new AI'agent' called Gato that can complete 604 different tasks'across a wide range of environments'. Gato uses a single neural network – a computing system with interconnected nodes that works like nerve cells in the human brain.
This course covers the main aspects of neural networks and deep learning. If you take this course, you can do away with taking other courses or buying books on R based data science. In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in neural networks and deep learning in R, you can give your company a competitive edge and boost your career to the next level! My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate.
When people think of artificial intelligence, the images that often come to mind are of the sinister robots that populate the worlds of "The Terminator," "i, Robot," "Westworld," and "Blade Runner." For many years, fiction has told us that AI is often used for evil rather than for good. But what we may not usually associate with AI is art and poetry -- yet that's exactly what Ai-Da, a highly realistic robot invented by Aidan Meller in Oxford, central England, spends her time creating. Ai-Da is the world's first ultra-realistic humanoid robot artist, and on Friday she gave a public performance of poetry that she wrote using her algorithms in celebration of the great Italian poet Dante. The recital took place at the University of Oxford's renowned Ashmolean Museum as part of an exhibition marking the 700th anniversary of Dante's death.
Eventbrite - Jayne Bullock, Department of Computer Science, University of Oxford (email@example.com) presents Strachey Lecture: Professor Neil Lawrence (University of Cambridge) - Tuesday, 3 May 2022 at Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, Oxford, England. Find event and ticket information.
In the Author Spotlight series, TDS Editors chat with members of our community about their career path in data science, their writing, and their sources of inspiration. Today, we're thrilled to share our conversation with Dr. Varshita Sher. Dr. Sher is currently working as a data scientist at the Alan Turing Institute's Applied Research Centre, leveraging deep-learning technology to solve problems in the NLP and Computer Vision domains. She has a Master's degree in Computer Science from the University of Oxford and a Ph.D. in Learning Analytics from Simon Fraser University. Her work in the last eight years has focused on the intersection of research and implementation of AI/ML algorithms in myriad sectors, including Edtech, Fintech, and Healthcare.
Robots are becoming a more and more important part of our home and work lives and as we come to rely on them, trust is of paramount importance. Successful teams are founded on trust, and the same is true for human-robot teams. But what does it mean to trust a robot? I'll be chatting to three roboticists working on various aspects of trustworthiness in robotics: Anouk van Maris (University of the West of England), Faye McCabe (University of Birmingham), Daniel Omeiza (University of Oxford). Anouk van Maris is a research fellow in responsible robotics.
Could artificial intelligence (AI) assessment have comparable diagnostic accuracy to clinician assessment for fracture detection? In a recently published meta-analysis of 42 studies, the study authors noted 92 percent sensitivity and 91 percent specificity for AI in comparison to 91 percent sensitivity and 92 percent specificity for clinicians based on internal validation test sets. For the external validation test sets, clinicians had 94 percent specificity and sensitivity in comparison to 91 percent specificity and sensitivity for AI, according to the study. In essence, the study authors found no statistically significant differences between AI and clinician diagnosis of fractures. "The results from this meta-analysis cautiously suggest that AI is noninferior to clinicians in terms of diagnostic performance in fracture detection, showing promise as a useful diagnostic tool," wrote Dominic Furniss, DM, MA, MBBCh, FRCS(Plast), a professor of plastic and reconstructive surgery in the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences at the Botnar Research Centre in Oxford, United Kingdom., and colleagues.
Trusted AI is when we discuss how to ensure and inject dimensions of trust into our intelligent systems, including fairness, robustness, accountability & responsibility, ethics, reliability and transparency. "Trust is the social glue that enables humankind to progress through interaction with each other and the environment, including technology" – Rachel Botsman, Trust Researcher & Trust Fellow at Oxford University How Trusted Does Your AI Need to Be? Now that we know what trusted AI is and what may cause the trust issues – How do we gain this trust in AI? How can we create trusted AI? Trusted AI Framework Capgemini realised there was a big problem around businesses not trusting AI, if they do not trust the outcome, they will not invest and buy it. Trusted AI is when we discuss how to ensure and inject dimensions of trust into our intelligent systems, including fairness, robustness, accountability & responsibility, ethics, reliability and transparency. "Trust is the social glue that enables humankind to progress through interaction with each other and the environment, including technology" – Rachel Botsman, Trust Researcher & Trust Fellow at Oxford University How Trusted Does Your AI Need to Be? Now that we know what trusted AI is and what may cause the trust issues – How do we gain this trust in AI? How can we create trusted AI? Capgemini realised there was a big problem around businesses not trusting AI, if they do not trust the outcome, they will not invest and buy it.
We have known for some time now that COVID-19 can affect the nervous system. Some people who contracted the SARS-CoV-2 virus have suffered from a number of neurological complications including confusion, strokes, impaired concentration, headaches, sensory disturbances, depression, and even psychosis, months after the initial infection. Now, researchers at the University of Oxford have conducted the first major peer-reviewed study comparing the brain scans of 785 people, aged 51 to 81 of whom 401 had contracted COVID and 384 had not. There were, on average, 141 days between testing positive for COVID and the second brain scan. The study revealed that, when compared to the scans of a control group, those who tested positive for COVID had greater overall brain shrinkage and more grey matter shrinkage and tissue damage in regions linked to smell and mental capacities months after the initial infection. Although the research does shed some light on the ongoing symptoms of long COVID, I would caution against generalising the findings to the population at large before more research is conducted.