Cerebras has shown off the capabilities of its second–generation wafer–scale engine, announcing it has set the record for the largest AI model ever trained on a single device. For the first time, a natural language processing network with 20 billion parameters, GPT–NeoX 20B, was trained on a single device. A new type of neural network, the transformer, is taking over. Today, transformers are mainly used for natural language processing (NLP) where their attention mechanism can help spot the relationship between words in a sentence, but they are spreading to other AI applications, including vision. The bigger a transformer is, the more accurate it is.
With the growth of artificial intelligence and machine learning in healthcare, even prosthetic limbs are becoming smart. These smart prosthetics can combine manual control with machine learning for more accessible and effective use. We are seeing a growth of machine learning in healthcare, where it is used to improve a patient's overall health, including providing accurate diagnosis and better treatment plans. Additionally, machine learning (ML) can also understand healthcare data by improving diagnostics and predicting accurate outcomes. One of the latest fields where AI and ML have been making an impact is prosthetics.
This article was published as a part of the Data Science Blogathon. You might be wandering in the vast domain of AI, and may have come across the word Exploratory Data Analysis, or EDA for short. Is it something important, if yes why? If you are looking for the answers to your question, you're in the right place. Also, I'll be showing a practical example of an EDA I did on my dataset recently, so stay tuned! Exploratory Data Analysis is the critical process of conducting initial investigations on data to discover patterns, spot anomalies, test hypotheses, and check assumptions with the help of summary statistics and graphical representations.
Douglas Hofstadter, a cognitive scientist, recently wrote in the Economist that he believes that GPT-3 is "cluelessly clueless." By this he means that GPT-3 has no idea about what it is saying. To illustrate, he and a colleague asked it a few questions. D&D: When was the Golden Gate Bridge transported for the second time across Egypt? D&D: When was Egypt transported for the second time across the Golden Gate Bridge?
A few months before COVID shut the world down in 2020, I published a book called The Future of Another Timeline. Set in 2022, it's about a group of time travelers who live in an alternate United States where abortion was never legalized. Working in secret, they travel 130 years back to the 19th century to foment protests against the anti-abortion crusader Anthony Comstock. Their goal is to change the course of history. When they return to 2022, abortion is legal in a few states, though it remains illegal in the majority of them.
Babies can help unlock the next generation of artificial intelligence (AI), according to Trinity neuroscientists and colleagues who have just published new guiding principles for improving AI. The research, published today in the journal Nature Machine Intelligence, examines the neuroscience and psychology of infant learning and distills three principles to guide the next generation of AI, which will help overcome the most pressing limitations of machine learning. Dr. Lorijn Zaadnoordijk, Marie Sklodowska-Curie Research Fellow at Trinity College explained: "Artificial intelligence (AI) has made tremendous progress in the last decade, giving us smart speakers, autopilots in cars, ever-smarter apps, and enhanced medical diagnosis. These exciting developments in AI have been achieved thanks to machine learning which uses enormous datasets to train artificial neural network models. "However, progress is stalling in many areas because the datasets that machines learn from must be painstakingly curated by humans.
A study, published in the European Heart Journal–Digital Health, shows the predictive potential of a deep-learning model in identifying patients at risk of atrial fibrillation (AF) following monitoring with a 24-hour ambulatory electrocardiogram (ECG), despite no documented prior AF, according to researchers. Led by Jagmeet Singh (Harvard Medical School, Boston, USA) the study involved training Cardiologs' deep neural network to predict the near-term presence or absence of AF by only using the first 24 hours of an extended Holter recording. Results showed that the network was able to predict whether AF would occur in the near future with an area under the receiver operating curve, sensitivity, and specificity of 79.4%, 76%, and 69%, respectively, and outperformed ECG features previously shown to be predictive of AF. These results showed a ten-point improvement compared to a baseline model using age and sex, researchers suggested. The study is the first of its kind to demonstrate the capability of artificial intelligence in predicting AF in the short-term using 24-hour Holter compared to resting 12-lead ECGs, the developer of the deep-learning model, Cardiologs, said in a press release.
We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. What if your doctor could instantly test dozens of different treatments to discover the perfect one for your body, your health and your values? In my lab at Stanford University School of Medicine, we are working on artificial intelligence (AI) technology to create a "digital twin": a virtual representation of you based on your medical history, genetic profile, age, ethnicity, and a host of other factors like whether you smoke and how much you exercise. If you're sick, the AI can test out treatment options on this computerized twin, running through countless different scenarios to predict which interventions will be most effective. Instead of choosing a treatment regimen based on what works for the average person, your doctor can develop a plan based on what works for you.