Well File:

Memory-Based Learning


IBM Watson Health Introduces New Opportunities for Imaging AI Adoption

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Orchestration--of AI and of workflow--offers a new way to help imaging organizations improve radiologists' reading experience while significantly reducing the impact on IT IBM (NYSE: IBM) Watson Health is introducing a new AI orchestration offering to help imaging organizations experience the benefits of having AI applications work seamlessly together. IBM Watson Health will officially launch IBM Imaging AI Orchestrator at the Radiological Society of North America (RSNA) 2021 Annual Meeting in Chicago this week. In addition, IBM is announcing IBM Imaging Workflow Orchestrator with Watson, a new solution that modernizes the radiologist's reading experience while reducing the demands on IT and imaging system administrators. "We recognize that when it comes to applying AI in imaging, it's hard to go it alone," said David Gruen, MD, MBA, FACR, Chief Medical Officer, Imaging, Watson Health. "Because each AI application is developed in a unique way with a specific purpose, it can be challenging for organizations to review and assess each one, and then to deploy them in a way that's beneficial to radiologists and their patients. That's why, with the rapid proliferation of approved algorithms, staffing shortages, and complexity of disease, the IBM Imaging AI Orchestrator could not come at a better time."


IBM Watson is AI for business - Drive Real Business Transformation

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For AI to thrive and for businesses to reap its benefits, it needs to be built on principles of trust. Watson is AI that you can understand and feel confident about because it provides the tools to help explain and manage AI-led decisions in your business. At IBM, your data and insights belong to you. That's the confidence you can pass onto your team and your customers.


IBM Watson and the future of Artificial Intelligence

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Watson, a supercomputer by IBM, shot to fame in 2011 as the'brain' that beat two of the best contestants of Jeopardy! to win a million dollars. This system that combines artificial intelligence (AI) and sophisticated analytical software to answer questions was widely deployed in many industries. The supercomputer was developed in IBM's DeepQA project and was named after IBM's founder Thomas J. Watson. "You can be discouraged by failure, or you can learn from it. So go ahead and make mistakes, make all you can. Because, remember that's where you'll find success -- on the far side of failure."


The New IBM Watson Assistant Is Available

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Currently all actions created in the bot are included in the deployment version. I would like to be able, to select specific Actions, and only deploy selected Actions and not all actions in the bot. An orchestration layer managing or combining different bots might also be helpful. Within a bot, there will be various actions. You will get to a situation where you do not want to duplicate actions across bots, and use multiple bots simultaneously in one implementation.


On the Optimal Memorization Power of ReLU Neural Networks

arXiv.org Machine Learning

We study the memorization power of feedforward ReLU neural networks. We show that such networks can memorize any $N$ points that satisfy a mild separability assumption using $\tilde{O}\left(\sqrt{N}\right)$ parameters. Known VC-dimension upper bounds imply that memorizing $N$ samples requires $\Omega(\sqrt{N})$ parameters, and hence our construction is optimal up to logarithmic factors. We also give a generalized construction for networks with depth bounded by $1 \leq L \leq \sqrt{N}$, for memorizing $N$ samples using $\tilde{O}(N/L)$ parameters. This bound is also optimal up to logarithmic factors. Our construction uses weights with large bit complexity. We prove that having such a large bit complexity is both necessary and sufficient for memorization with a sub-linear number of parameters.


IBM Watson and the future of Artificial Intelligence by Procommun

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Buy Now Pay Later (BNPL) services have increased in popularity in recent years and are ready to become a popular mode of financing. Experts claim that demand for BNPL has been accelerating in India for the past three to four years. Further, COVID-19 has boosted its demand. BNPL has now established itself as a more comfortable payment option, reducing borrowers' financial stress by providing no-cost EMIs. Uni Cards, which recently secured $18.5 million in financing, has launched its Uni Pay 1/3rd card. The product aims to enhance the customer experience in the credit card business.


How Square Is Using Artificial Intelligence and Machine Learning to Improve Cash App

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Today, I continue my top-AI-stocks video series. If you are new to this series, it covers my top 12 artificial intelligence stocks focused on growth and disruptive innovation. I have done my best to find the highest-growth companies in a variety of sectors with disruptive growth trends. Last time, I shared my favorite chatbot stock. In today's video, I am covering an unknown business that Square (NYSE:SQ) acquired in 2020 that focuses on artificial intelligence and machine learning.




DisCERN:Discovering Counterfactual Explanations using Relevance Features from Neighbourhoods

arXiv.org Artificial Intelligence

Counterfactual explanations focus on "actionable knowledge" to help end-users understand how a machine learning outcome could be changed to a more desirable outcome. For this purpose a counterfactual explainer needs to discover input dependencies that relate to outcome changes. Identifying the minimum subset of feature changes needed to action an output change in the decision is an interesting challenge for counterfactual explainers. The DisCERN algorithm introduced in this paper is a case-based counter-factual explainer. Here counterfactuals are formed by replacing feature values from a nearest unlike neighbour (NUN) until an actionable change is observed. We show how widely adopted feature relevance-based explainers (i.e. LIME, SHAP), can inform DisCERN to identify the minimum subset of "actionable features". We demonstrate our DisCERN algorithm on five datasets in a comparative study with the widely used optimisation-based counterfactual approach DiCE. Our results demonstrate that DisCERN is an effective strategy to minimise actionable changes necessary to create good counterfactual explanations.