If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The arguments / discussions between the Bayesian vs frequentist approaches in statistics are long running. I am interested in how these approaches impact machine learning. Often, books on machine learning combine the two approaches, or in some cases, take only one approach. This does not help from a learning standpoint. So, in this two-part blog we first discuss the differences between the Frequentist and Bayesian approaches.
The increasingly rapid movement of technology out of back offices and into the hands of most of the world's population has profound effects. Even though the pandemic is widely credited with accelerating digital uptake, the full progression is really just beginning to shift from linear to exponential. As bank technology consultant Shanker Ramamurthy likes to point out, most of the world's population now walks around with "trillion-dollar" computers in their pockets. Ramamurthy is Global Managing Partner, Banking for IBM's Global Business Services group, the giant tech company's banking and consulting practice. Adding to that, IBM itself has been humbled by the same forces of digital transformation impacting the businesses it serves.
A decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.
With AI use continuing to grow in adoption throughout enterprise IT, Deloitte is creating a new Deloitte Center for AI Computing to advise its customers, explain the technology and help them use it in their ongoing business and growth plans. Designed to provide a cloud-accessible accelerated platform that Deloitte clients can use to test and explore various AI strategies and tools, the infrastructure features six Nvidia DGX A100 systems, Nvidia Mellanox networking, high-performance storage and Nvidia software. The platform will be physically hosted in Deloitte's datacenter in Hermitage, Tenn., but will also be part of a virtual service. Based on Nvidia's DGX POD reference architecture and harnessing the power of 48 A100 GPUs, the installation will serve as an AI acceleration engine for Deloitte's clients, the company said. The new facility was announced on Tuesday (March 2).
Whereas many AI models are trained on carefully labelled datasets, Facebook said SEER learned how to identify objects in photos by analyzing random, unlabeled and uncurated Instagram images. This AI technique is known as self-supervised learning. "The future of AI is in creating systems that can learn directly from whatever information they're given -- whether it's text, images, or another type of data -- without relying on carefully curated and labeled data sets to teach them how to recognize objects in a photo, interpret a block of text, or perform any of the countless other tasks that we ask it to," Facebook's researchers wrote in a blog post. "SEER's performance demonstrates that self-supervised learning can excel at computer vision tasks in real-world settings," they added. "This is a breakthrough that ultimately clears the path for more flexible, accurate, and adaptable computer vision models in the future."
Armed with computer vision and advanced sensors, artificial intelligence drones can continually identify and monitor their surroundings and react accordingly. FREMONT, CA: Drones are unmanned aerial devices used for many applications. When first developed, these devices were manually controlled. However, drones incorporate artificial intelligence, automating some or all processes. The incorporation of AI allows drones to use data from sensors attached to the drone to gather and deploy visual and environmental data.
Generative Pre-trained Transformer 3 is an autoregressive language model that uses deep learning to produce human-like text. It is the third-generation of language prediction model in the GPT-n series created by OpenAI. GPT-3 is an extension and scaled-up version of GPT-2 model architecture -- It includes the modified initialization, pre-normalization, and reversible tokenization and shows strong performance on many NLP tasks in the zero-shot, one-shot, and few-shot settings. In the above graph, it is clearly visible how GPT-3 dominates all the small models and gets substantial gains on almost all the NLP tasks. It is based on the approach of pretraining on a large dataset followed by fine-tuning or priming for a specific task.
Portfolio optimization is the process of choosing the best portfolio among the set of all portfolios. The naive way is to select a group of random allocations and figure out which one has the best Sharpe Ratio. This is known as the Monte Carlo Simulation where randomly a weight is assigned to each security in the portfolio and then the mean daily return and standard deviation of daily return is calculated. This helps in calculating the Sharpe Ratio for randomly selected allocations. But the naive way is time taking so an optimization algorithm is used which works on the concept of the minimizer.
Imagine if your digital marketing tools had the capacity to predict the future. What would you do with that crystal ball? Or providing each user a set of search results that have shown to be the most likely to yield a conversion? Recommending a product through a web campaign that can be most effective to prompt an engagement? This is where artificial intelligence is most effective for digital marketers.
There haven't been many technological breakthroughs in recent history that are as significant and as impactful as artificial intelligence. Gone are the days wherein robots and machine learning were only ideas to be found in science fiction novels. Not only has artificial intelligence made some really great strides as a discipline, but more and more members of the masses have adopted artificial intelligence into their everyday life. In the past, everyone would think of movies like the Terminator or iRobot when it came to talks of artificial intelligence. But now, sophisticated AI software can be found in the everyday devices that we use. Now, you might be curious how AI is applied in the modern world these days.