Cyber fraud using famous names

 This ad appeared on MSN, Microsoft's news website. I later verified this. See the screenshot below. It appeared directly on Microsoft's site, not on some random website.

The ad claimed that Nandan Nilekani had a novel plan to make Indians rich with the help of AI. When I clicked the ad, it took me to a page that looked identical to the Indian Express news page, apparently targeting innocent investors. I skimmed the content but didn't delve into the details. Later, I noticed the URL in the address bar – it wasn't an Indian Express URL. Read more

McKinsey insights for CTOs for Generative AI

Through conversations with dozens of tech leaders and an analysis of generative AI initiatives at more than 50 companies (including our own), we have identified nine actions all technology leaders can take to create value, orchestrate technology and data, scale solutions, and manage risk for generative AI (see sidebar, “A quick primer on key terms”):

Move quickly to determine the company’s posture for the adoption of generative AI, and develop practical communications to, and appropriate access for, employees.

Reimagine the business and identify use cases that build value through improved productivity, growth, and new business models. Develop a “financial AI” (FinAI) capability that can estimate the true costs and returns of generative AI.

https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/technologys-generational-moment-with-generative-ai-a-cio-and-cto-guide

5 ways to learn AI

 Authors found that in their survey sales leaders admitted to a surprising lack of familiarity with generative AI. Only 21% of them professed substantial knowledge, which put them behind leaders working in operations (26%), general management (40%), HR (44%), purchasing (57%), and product/engineering (60%) — and, not surprisingly, far behind leaders in IT (80%). Marketing and sales leaders also reported the lowest utilization of generative AI for work-related tasks, with only 35% reporting using it at least sometimes at work. This is concerning, given how important intentional experimentation is to determining the relevance and usefulness of new technologies.

https://hbr.org/2023/11/5-ways-marketing-and-sales-leaders-can-embrace-genai

Our book with Wiley on AI

Thanks, Nisha. Thanks for your kind words. I learned a lot from you, Wil and Michael. I enjoyed being your editor! I benefited greatly from the many conversations we had on leading AI and data in a large legacy organization. ...

Explainable AI: Getting it right in business | McKinsey

https://www.mckinsey.com/capabilities/quantumblack/our-insights/why-businesses-need-explainable-ai-and-how-to-deliver-it 

Astra Zeneca ML Ops - Insight into Patient sentiments

AstraZeneca analyzes anonymized patient data to gain insights including learning patient sentiments on specific treatments and feeding progression models on therapeutic areas such as chronic kidney disease, heart failure readmission, and cancer classification. AstraZeneca delivers those insights to commercial analysts, who in turn use them to improve business processes and drive awareness and uptake with healthcare systems. “We focus on targeting alignment and marketing, helping our commercial teams deliver the right information to healthcare providers to address patient needs,” says Cherry Cabading, global senior enterprise architect at AstraZeneca.

https://aws.amazon.com/solutions/case-studies/AstraZeneca-case-study/

Bata uses AI for in-store analytics

Video analytics and emotional AI for Bata: The footwear retail company Bata deployed an AI-based video analytics solution to improve the company's in-store sales, operations, and customer satisfaction. Developed by Agrex.ai, the solution utilized the store's existing video infrastructure to implement data harvesting and insight generation on smart conversion and audience segmentation. An "Emotions Chart" adjudged the number of customers showing interest in the merchandise and the type of merchandise. The store compared the responses to products..

https://indiaai.gov.in/article/indian-retail-brands-using-ai-5-case-studies

Watch "Fmr. Google CEO Eric Schmidt on the Consequences of an A.I. Revolution | Amanpour and Company" on YouTube

https://youtu.be/Sg3EchbCcA0 

This Is How PayPal Uses AI/ML To Manage $712 Billion Payments

Primarily PayPal uses logistics regression for fraud detection.. 

For the tech stack, PayPal runs one of the most massive hybrid-cloud environments in the world. It hosts multiple technologies in each layer and function. Around 2700 applications are running over 20000 servers with 238 PetaBytes of storage.

https://analyticsindiamag.com/this-is-how-paypal-uses-ai-ml-to-manage-712-billion-payments/

Google Brain's AI achieves state-of-the-art text summarization performance | VentureBeat

Not to be outdone, a Google Brain and Imperial College London team built a system — Pre-training with Extracted Gap-sentences for Abstractive SUmmarization Sequence-to-sequence, or Pegasus — that leverages Google’s Transformers architecture combined with pretraining objectives tailored for abstractive text generation. They say it achieves state-of-the-art results in 12 summarization tasks spanning news, science, stories, instructions, emails, patents, and legislative bills, and that it shows “surprising” performance on low-resource summarization, surpassing previous top results on six data sets with only 1,000 examples.



https://venturebeat.com/2019/12/23/google-brains-ai-achieves-state-of-the-art-text-summarization-performance/

An algorithm that learns through rewards may show how our brain does too - MIT Technology Review

 DeepMind partnered with a group at Harvard to observe dopamine neuron behavior in mice. They set the mice on a task and rewarded them based on the roll of dice, measuring the firing patterns of their dopamine neurons throughout. They found that every neuron released different amounts of dopamine, meaning they had all predicted different outcomes. While some were too "optimistic," predicting higher rewards than actually received, others were more "pessimistic," lowballing the reality. When the researchers mapped out the distribution of those predictions, it closely followed the distribution of the actual rewards. This data offers compelling evidence that the brain indeed uses distributional reward predictions to strengthen its learning algorithm.

https://www.technologyreview.com/s/615054/deepmind-ai-reiforcement-learning-reveals-dopamine-neurons-in-brain/

How Machine Learning Can Find Extremists on Social Media | Yale Insights

Zaman's team then tracked about 647,000 of the accounts for several months, and by September 2015, Twitter had suspended roughly 35,000 of them—presumably because those users had posted extremist content. So the researchers used AI to identify typical features of suspended accounts. For example, following certain users or concealing one's location was linked to a higher likelihood of extremism.

https://insights.som.yale.edu/insights/how-machine-learning-can-find-extremists-on-social-media

Machine vision has learned to use radio waves to see through walls and in darkness - MIT Technology Review

Enter Tianhong Li and colleagues at MIT, who have found a way to teach a radio vision system to recognize people's actions by training it with visible-light images. The new radio vision system can see what individuals are up to in a wide range of situations where visible-light imaging fails. "We introduce a neural network model that can detect human actions through walls and occlusions, and in poor lighting conditions," say Li and co.

https://www.technologyreview.com/s/614470/machine-vision-has-learned-to-use-radio-waves-to-see-through-walls-and-in-darkness/

Baidu has a new trick for teaching AI the meaning of language - MIT Technology Review

researchers trained ERNIE on a new version of masking that hides strings of characters rather than single ones. They also trained it to distinguish between meaningful and random strings so it could mask the right character combinations accordingly. As a result, ERNIE has a greater grasp of how words encode information in Chinese and is much more accurate at predicting the missing pieces. This proves useful for applications like translation and information retrieval from a text document.

https://www.technologyreview.com/s/614996/ai-baidu-ernie-google-bert-natural-language-glue/

Google Meena bot

Meena is a fantastic contribution to the chatbot space. It is hard to capture the enormity of the task Google has achieved here. But we need to be careful about how we communicate the results of that research. Descriptions such as "the bot that can chat about anything" or "the best chatbot" are not necessarily useful. They distract from what is really important about this research — defining human-like conversation and exploring what role or importance there is in the chatbot world for that type of conversation.

Reuters AI news Tracer

The system processes 12 million tweets every day, rejecting almost 80 percent of them as noise. The rest fall into about 6,000 clusters that the system categorizes as different types of news events. That’s all done by 13 servers running 10 different algorithms.

By comparison, Reuters employs some 2,500 journalists around the world who together generate about 3,000 news alerts every day, using a variety of sources, including Twitter. Of these, around 250 are written up as news stories.

read more here - https://www.technologyreview.com/s/609558/how-reuterss-revolutionary-ai-system-gathers-global-news/

Why Some AI Efforts Succeed While Many Fail

A common mistake companies make is to assume that their AI strategy should be considered primarily from a technology perspective. As a result, their AI efforts have an IT- or data-analyst-centric focus. This is the wrong approach. The companies that derive the most value are those that view AI as a core pillar of their overall business strategy. Integrating AI into the business strategy ensures that AI initiatives get the proper focus across the organization, in particular with the CEO and other senior company executives, without whose sponsorship and support it's nearly impossible for any transformative technology to succeed.

https://blogs.wsj.com/cio/2020/01/24/why-some-ai-efforts-succeed-while-many-fail/?fbclid=IwAR3pNzm6-_N9aOKDj21ZIkwfP3nv9LVnfxFtkFhh-R6Q1bKDlFbUIb9Vdi4

5 Mind-Blowing Ways Facebook Uses Machine Learning - GeeksforGeeks

The Facebook News Feed was one addition that everybody hated initially but now everybody loves!!! And if you are wondering why some stories show up higher in your Facebook News Feed and some are not even displayed, well here is how it works! Different photos, videos, articles, links or updates from your friends, family or businesses you like show up in your personal Facebook News Feed according to a complex system of ranking that is managed by a Machine Learning algorithm.

The rank of anything that appears in your News Feed is decided on three factors. Your friends, family, public figures or businesses that you interact with a lot are given top priority. Your feed is also customized according to the type of content you like (Movies, Books, Fashion, Video games, etc.) Also, posts that are quite popular on Facebook with lots of likes, comments and shares have a higher chance of appearing on your Facebook News Feed.

https://www.geeksforgeeks.org/5-mind-blowing-ways-facebook-uses-machine-learning/

Who needs Copernicus if you have machine learning? - MIT Technology Review

The laws of physics are simple representations that can be interrogated to provide information about more complex scenarios. Imagine setting a pendulum in motion and asking where the base of the pendulum will be at some point in the future. One way to answer this is by measuring the position of the pendulum as it swings. This data can then be used as a kind of look-up table to find the answer. But the laws of motion provide a much easier way of discovering the answer: simply plug values for the various variables into the appropriate equation. That gives the correct answer too. That's why the equation can be thought of as a compressed representation of reality.

https://www.technologyreview.com/s/611798/who-needs-copernicus-if-you-have-machine-learning/

Augmented reality in retail: Virtual try before you buy - MIT Technology Review

Nike Fit, a scanning app, uses a combination of computer vision, data science, machine learning, artificial intelligence and recommendation algorithms to measure the full shape of a user's feet and know the perfect fit for each Nike shoe style.

The app collects data on 13 points on a person's foot within a matter of seconds. This foot dimension can be stored in a user's NikePlus account for future shopping both online and in store.

https://www.technologyreview.com/s/614616/augmented-reality-in-retail-virtual-try-before-you-buy/