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Healthcare AI in a year: 3 trends to watch

#artificialintelligence

Between the COVID-19 pandemic, a mental health crisis, rising healthcare costs, and aging populations, industry leaders are rushing to develop healthcare-specific artificial intelligence (AI) applications. One signal comes from the venture capital market: over 40 startups have raised significant funding--$20M or more --to build AI solutions for the industry. But how is AI actually being put to use in healthcare? The "2022 AI in Healthcare Survey" queried more than 300 respondents from across the globe to better understand the challenges, triumphs, and use cases defining healthcare AI. In its second year, the results did not change significantly, but they do point to some interesting trends foreshadowing how the pendulum will swing in years to come.


10 Key Roles For AI Success - AI Summary

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"This person is tasked with packing the ML model into a container and deploying to production -- usually as a microservice," says Dattaraj Rao, innovation and R&D architect at technology services company Persistent Systems. The role requires expert back-end programming and server configuration skills, as well as knowledge of containers and continuous integration and delivery deployment, Rao says. They are crucial to AI initiatives because data needs to be both collected and made suitable for consumption before anything trustworthy can be done with it, says Erik Gfesser, director and chief architect at Deloitte. This person is an authority in their domain, can judge the quality of available data, and can communicate with the intended business users of an AI project to make sure it has real-world value. When Babych's company developed a computer-vision system to identify moving objects for autopilots as an alternative to LIDAR, they started the project without a domain expert.


10 key roles for AI success

#artificialintelligence

More companies in every industry are adopting artificial intelligence to transform business processes. But the success of their AI initiatives depends on more than just data and technology -- it's also about having the right people on board. An effective enterprise AI team is a diverse group that encompasses far more than a handful of data scientists and engineers. Successful AI teams also include a range of people who understand the business and the problems it's trying to solve, says Bradley Shimmin, chief analyst for AI platforms, analytics, and data management at consulting firm Omdia. "The technologies and the tooling that we have available is skewing more and more toward enabling and empowering domain professionals, the business users, or the analytics professionals to take direct ownership of AI within companies," he says.


AI has a dangerous bias problem -- here's how to manage it

#artificialintelligence

Thomas covers AI in all its iterations. Writer at Neural by TNW -- Thomas covers AI in all its iterations. Proponents of the approach argue that it can eliminate human prejudices, but critics warn that algorithms can amplify our biases -- without even revealing how they reached the decision. This can result in AI systems leading to Black people being wrongfully arrested, or child services unfairly targeting poor families. The victims are frequently from groups that are already marginalized.


Machine learning and AI is coming for corrupt officials

#artificialintelligence

South Africa has a big problem with corruption in government supply chains. The most salient recent example would be the looting of funds during the Covid-19 pandemic, specifically the procurement of personal protective equipment in the Gauteng health department. Mark Heywood correctly asserted in the Daily Maverick that unless we introduce the certainty of punishment for corrupt public officials, we will lose the fight against corruption . The July looting and riots taught us that these events affect our daily lives. They cause job losses and food price increases and are especially hard on the youth sector.


Managing AI and data science: Practical lessons from big pharma

#artificialintelligence

Data science and artificial intelligence are adding a new dimension to drug discovery and development, emphasizing computation and machine learning. Given this shift, pharmaceutical companies are actively building infrastructure, data, tools, and teams to bring together data scientists with biology and life science experts. Pharma and biotech innovation offer a glimpse into how large organizations integrate AI tools and techniques with traditional subject matter experts who possess a deep understanding of the underlying problems to be solved. To gain an insider's perspective on how pharma companies use AI and machine learning, I invited Dr. Bülent Kızıltan to join episode #717 of the CXOTalk series of conversations with people shaping our world. He is Head of Causal & Predictive Analytics, Data Science & AI, at the Novartis AI Innovation Center.


Bach

AAAI Conferences

During the early stages of developing Case-Based Reasoning (CBR) systems the definition of similarity measures is challenging since this task requires to transfer implicit knowledge of domain experts into knowledge representations. While an entire CBR system is very explanatory, the similarity measure determines the ranking but do not necessarily show which features contribute to high (or low) rankings. In this paper we will present our work on opening the knowledge engineering process for similarity modelling. We will present how we transfer implicit knowledge from experts as well as a data-driven approach into case and similarity representations for CBR systems. The work present is a result of interdisciplinary research collaborations between AI and medical researchers developing e-Health applications.


"My data drifted. What's next?" How to handle ML model drift in production.

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"I have a model in production, and the data is drifting. That is a question we often get. This data drift might be the only signal. You are predicting something, but don't know the facts yet. Statistical change in model inputs and outputs is the proxy. The data has shifted, and you suspect a decay in the model performance. In other cases, you can know it for sure. You can calculate the model quality or business metrics. Accuracy, mean error, fraud rates, you name it. The performance got worse, and the data is different, too. What can you do next? Here is an introductory overview of the possible steps.


The 6-Ds of Creating AI-Enabled Systems

arXiv.org Artificial Intelligence

We are entering our tenth year of the current Artificial Intelligence (AI) spring, and, as with previous AI hype cycles, the threat of an AI winter looms. AI winters occurred because of ineffective approaches towards navigating the technology valley of death. The 6-D framework provides an end-to-end framework to successfully navigate this challenge. The 6-D framework starts with problem decomposition to identify potential AI solutions, and ends with considerations for deployment of AI-enabled systems. Each component of the 6-D framework and a precision medicine use case is described in this paper.


5 ways machine learning uses CI/CD in production

#artificialintelligence

Continuous integration (CI) is the process of all software developers merging their code changes in a central repository many times throughout the day. A fully automated software release process is called continuous delivery, abbreviated as CD. Although the two terms are not interchangeable, CI/CD is a DevOps methodology and fits in that category. A continuous integration/continuous delivery (CI/CD) pipeline is a system that automates the software delivery process. CI/CD pipelines generate code, run tests, and deliver new product versions when software is changed.