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Building trust in AI: getting the wizard out from behind the curtain - Journey to AI Blog

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Artificial Intelligence (AI) is often regarded as “Great and Powerful;” it can add tremendous value by transforming business workflows with faster, smarter decisions. At the same time, AI can be mysterious and even scary. In order to build trust, AI needs to be transparent and explainable: “out from behind the curtain” so to speak. As IBM’s recent study on AI Ethics found, corporate boards are looking to Data and Technology leaders to make that happen, and I couldn’t agree more. CDOs and CTOs can be instrumental in bringing forth both human value and human values in enterprise AI. Putting the human first To build trust in business AI, we must always put the value of the human first. This should happen at the data-provider level and the decision-maker level. At the provider level, building trust starts with data governance to ensure that the data itself can be trusted. In our organization, embedded within this is the IBM…


Microsoft announces new supercomputer, lays out vision for future AI work - The AI Blog

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Microsoft has built one of the top five publicly disclosed supercomputers in the world, making new infrastructure available in Azure to train extremely large artificial intelligence models, the company is announcing at its Build developers conference. Built in collaboration with and exclusively for OpenAI, the supercomputer hosted in Azure was designed specifically to train that company's AI models. It represents a key milestone in a partnership announced last year to jointly create new supercomputing technologies in Azure. It's also a first step toward making the next generation of very large AI models and the infrastructure needed to train them available as a platform for other organizations and developers to build upon. "The exciting thing about these models is the breadth of things they're going to enable," said Microsoft Chief Technical Officer Kevin Scott, who said the potential benefits extend far beyond narrow advances in one type of AI model.


Is OpenAI's GPT-3 is something to fear?

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After they published the article, many responses came from different media houses and notable people trying to shed some light on what exactly happened. First, let's look at the legitimacy of the article. Surely it was the generated one but with no human intervention? So, it is just another media overhype? I mean cherry-picking the best and presenting it to you in a way that sells.


IBM secures fifth consecutive year of AI Software Platform market share leadership, says new IDC report - Journey to AI Blog

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For the fifth consecutive year, IDC ranked IBM the #1 market share leader in AI software platforms for 2019. In the IDC report, Worldwide AI Software Platforms Market Shares, 2019: The Battle Has Begun (doc #US46652020, July 2020), IDC valued the AI software platform market at USD 3.5 billion in 2019, a near-30% increase over the prior year. And despite a crowded landscape of competitors, IDC finds IBM leading the field among the largest AI platform players with an 8.8% share. While COVID-19 forces companies worldwide to reconsider business as usual, the accolades can wait; there's no time for a victory lap when work remains to accelerate the COVID-19 economic recovery with Data and AI. With IBM Watson positioned as the business world's first choice in AI software platforms, four competitive differentiators distinguish it from the competition.


Topics in data analysis

AIHub

This series of blog posts is based on the Fall 2019 10-718 Data Analysis class at Carnegie Mellon University, taught by Leila Wehbe, with the assistance of Jacob Tyo, Aria Wang and Fabricio Flores. The blog posts were written by the students and edited by the instructors and the ML@CMU blog team. A simple definition is: the application of machine learning and statistical methods to real world data to solve a problem. While this statement is simple, data analysis eventually requires expertise from a vast number of disciplines such as the real world domain in question (e.g. The complexity of data science leads to a plethora of possible pitfalls, with no clear instructions on how to avoid them.


Multiple Instance Learning

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When it comes to applying computer vision in the medical field, most tasks involve either 1) image classification for diagnosis or 2) segmentation to identify and separate lesions. However, in pathology cancer detection, this is not always possible. Obtaining labels is time-consuming and labor-intensive. Furthermore, pathology slides can be up to 200k x 100k pixels resolution, and they will not fit in memory for classification since for example, the ImageNet only uses 224 x 224 pixels for training. Downsampling normally is not an option because we are trying to detect a tiny area, such as a cancerous area varying from 300 x 300 pixels area (a few dots in Figure 1).


What Does 2500+ Popular Article's Historical Data Teach Us?

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A few days back when I wrote my second article on Medium, I gave it to my friends for review. One of them immediately told me that the title is very captive and that can pull the readers, I didn't take his words seriously and thought he was just encouraging me. I finally published it on a Saturday afternoon. It reached 70 views that day. The next morning when I woke up to check if I had done any better than my first blog, my blog managed to reach 120 views.


What Every Board Director And CEO Need To Read To Advance Their AI Knowledge

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Every Board Director and CEO need to accelerate their Learning in AI and Machine Learning to Manage ... [ ] Risk. Time for Something New is Now! Are you keeping informed by reading on a regular basis, here are a few guide posts to help you speed up your AI learning as a board director, CEO or senior executive striving to advance your knowledge in the Intelligence Revolution - where AI is simply everywhere! With the speed of AI content proliferating the market, and media channels growing at over 50% a years, and by 2021, over 80% of all new emerging software technologies will apply AI in some fashion in their business models, according to Gartner Group. As of August 2020, IDC reported that the AI market - including software, hardware, and services, are forecast to grow 12.3 percent to $156.5 billion in 2020. Worldwide AI revenues will surpass $300 billion in 2024 with a five-year compound annual growth rate (CAGR) of over seventeen percent.


Artificial intelligence: an incidental approach to figuring out the human consciousness, an…

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An honorable mention to the AI podcast by Lex Fridman for interviewing such inspiring scientists and pioneers in the world of AI, was definitely an inspiration to this particular post on this blog.


Microsoft 365 saves you time and effort with transcription and voice commands in Word - Microsoft 365 Blog

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Now more than ever, we're all very busy--juggling family, work, friends, and whatever else life throws our way. New enhancements in Office leverage the Azure Cognitive Services AI platform so you can harness the power of your voice to spend less time and energy creating your best work and focus on what matters most. Whether you're a reporter conducting interviews, a researcher recording focus group sessions, or an online entrepreneur recording informal discussions, you want to be able to focus on the people you're talking to without worrying about taking notes and without having to spend hours transcribing your conversations after-the-fact. If that sounds like you, Transcribe in Word is here to help. Now you can record your conversations directly in Word for the web and transcribe them automatically.