Tag: machine learning
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MIT researchers show how to detect and address AI bias without loss in accuracy
The key…is often to get more data from underrepresented groups. For example…an AI model was twice as likely to label women as low-income and men as high-income. By increasing the representation of women in the dataset by a factor of 10, the number of inaccurate results was reduced by 40 percent. Source: MIT researchers show…
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The AI Threat to Democracy
With the advent of strong reinforcement learning…, goal-oriented strategic AI is now very much a reality. The difference is one of categories, not increments. While a supervised learning system relies upon the metrics fed to it by humans to come up with meaningful predictions and lacks all capacity for goal-oriented strategic thinking, reinforcement learning systems…
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When AI Misjudgment Is Not an Accident
in AIThe conversation about unconscious bias in artificial intelligence often focuses on algorithms that unintentionally cause disproportionate harm to entire swaths of society…But the problem could run much deeper than that. Society should be on guard for another twist: the possibility that nefarious actors could seek to attack artificial intelligence systems by deliberately introducing bias into…
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The fate of medicine in the time of AI
Source: Coiera, E. (2018). The fate of medicine in the time of AI. The challenges of real-world implementation alone mean that we probably will see little change to clinical practice from AI in the next 5 years. We should certainly see changes in 10 years, and there is a real prospect of massive change in…
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Rodney Brooks | Robotics & AI – Their Present & Future
Rodney Brooks was one of the leading developers of AI coding tools throughout the 80s and early 90s at MIT, where he spent a decade running one of the two largest and most prominent AI centres in the world. There are few who can match the breadth, depth, and duration of Rodney’s purview on the…
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adapting to constant change
The human work of tomorrow will not be based on competencies best-suited for machines, because creative work that is continuously changing cannot be replicated by machines or code. While machine learning may be powerful, connected human learning is novel, innovative, and inspired. Source: Jarche, H. (2018). adapting to constant change. A good post on why…
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Facebook and NYU Using AI to Speed Up MRIs
The Facebook/NYU partnership is working to minimize the amount of data that is captured, instead relying on computers to reconstruct the image from imperfect inputs. If this is successful, we may see a 10x reduction in scan times, which would lead to lower costs for MRIs and a much greater utilization of these machines Source:…
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How to ensure safety for medical artificial intelligence
When we think of AI, we are naturally drawn to its power to transform diagnosis and treatment planning and weigh up its potential by comparing AI capabilities to those of humans. We have yet, however, to look at AI seriously through the lens of patient safety. What new risks do these technologies bring to patients,…
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‘The discourse is unhinged’: how the media gets AI alarmingly wrong
Zachary Lipton, an assistant professor at the machine learning department at Carnegie Mellon University, watched with frustration as this story transformed from “interesting-ish research” to “sensationalized crap”. According to Lipton, in recent years broader interest in topics like “machine learning” and “deep learning” has led to a deluge of this type of opportunistic journalism, which…
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How well can we actually predict the future? Katja Grace on why expert opinion isn’t a great guide to AI’s impact and how to do better – 80,000 Hours
Experts believe that artificial intelligence will be better than humans at driving trucks by 2027, working in retail by 2031, writing bestselling books by 2049, and working as surgeons by 2053. But how seriously should we take these predictions? Katja Grace, lead author of ‘When Will AI Exceed Human Performance?’, thinks we should treat such…
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Prof Allan Dafoe on trying to prepare the world for the possibility that AI will destabilise global politics
…even if we stopped at today’s AI technology and simply collected more data, built more sensors, and added more computing capacity, extreme systemic risks could emerge, including: 1) Mass labor displacement, unemployment, and inequality; 2)The rise of a more oligopolistic global market structure, potentially moving us away from our liberal economic world order; 3)Imagery intelligence…
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Dina Katabi: A new way to monitor vital signs (that can see through walls) | TED Talk
So if you think about it, wireless signals, they travel through space, they go through obstacles and walls and occlusions, and some of them, they reflect off our bodies, because our bodies are full of water, and some of these minute reflections, they come back. And if, just if, I had a device that can…
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DeepMind’s AI can detect over 50 eye diseases as accurately as a doctor.
This is the point at which the risk from medical AI becomes much greater. Our inability to explain exactly how AI systems reach certain decisions is well-documented. And, as we’ve seen with self-driving car crashes, when humans take our hands off the wheel, there’s always a chance that a computer will make a fatal error…
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The Future of Artificial Intelligence Depends on Trust
To open up the AI black box and facilitate trust, companies must develop AI systems that perform reliably — that is, make correct decisions — time after time. The machine-learning models on which the systems are based must also be transparent, explainable, and able to achieve repeatable results. Source: Rao, A. & Cameron, E. (2018).…
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MIT Creates AI to Optimize Brain Cancer Treatment
The goal [with chemotherapy] is basically to poison the tumor cells faster than non-cancerous cells, but the side effects of going after an aggressive disease like this can be devastating. These traditional treatment schedules don’t take into account differences in tumor size, medical histories, genetic profiles, and biomarkers. The system developed by MIT does that,…
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Will Marshall: The mission to create a searchable database of Earth’s surface | TED Talk
in TechnologyAnd we now have over 200 satellites in orbit, downlinking their data to 31 ground stations we built around the planet. In total, we get 1.5 million 29-megapixel images of the Earth down each day. And on any one location of the Earth’s surface, we now have on average more than 500 images. A deep…
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A small team of student AI coders beats Google’s machine-learning code
in AIThe Fast.ai algorithm was trained on the ImageNet database in 18 minutes using 16 Amazon Web Service instances, at a total compute cost of around $40. Source: Knight, W. (2018). A small team of student AI coders beats Google’s machine-learning code. A few things that I took from this: Access to massive data sets is…
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The OpenAI Dota 2 bots just defeated a team of former pros – The Verge
in AIThose artificial intelligence agents learned everything by themselves, exploring and experimenting on the complex Dota playing field at a learning rate of 180 years per day Source: The OpenAI Dota 2 bots just defeated a team of former pros – The Verge Yet another important idea that’s often lost in the noise of reporting on…
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Fairness matters: Promoting pride and respect with AI
We’re creating an open dataset that collects diverse statements from the LGBTIQ+ community, such as “I’m gay and I’m proud to be out” or “I’m a fit, happy lesbian that has just retired from a wonderful career” to help reclaim positive identity labels. These statements from the LGBTIQ+ community and their supporters will be made…
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a16z Podcast: Putting AI in Medicine, in Practice
A wide-ranging conversation on several different aspects of AI in medicine. Some of the key takeaways for me included: AI (in it’s current form) has some potential for long-term prediction (e.g. you have an 80% chance of developing diabetes in the next 10 years) but we’re still very far from accurate short-term prediction (e.g. you’re…