Unilever's Use of AI in Recuitung

Late to this but have read Bernard Marr's book: AI in Practice:  How 50 successful Companies used AI and Machine Learning to Solve Problems, published in 2019, where he describes the use of AI by a number of competitive companies, notably competitor Unilever. Some of this is still dated now, but still notable. I reviewed as I looked at potential of big company applications. Here nicely overviewd, 

Unilever:   Using Artificial Intelligence to Steam lining recruiting and Onboarding  Page 129-

International consumer goods manufacturer Unilever sells over 400 branded products in 190 countries. Worldwide it has over 160,000 people, making it one of the worlds largest employers.

With any company, the people are the most valuable resources. To make sure they are enticing the right talent Unilever employs AI solutions aimed at attracting and  analyzing and selecting the best people to fit the thousands of roles it need to fill each year. 

Any recruiting process involves risk. Advertising for talent,  screening applicants and onboarding new hires is an expensive and  time consuming process. It has to be done properly though as hiring the people can have expensive consequences and a damaging impact on business.   ... ' 

Use cases of GPT-4

 Directions and examples.....

MIT Technology Review

ARTIFICIAL INTELLIGENCE

How AI experts are using GPT-4

Plus: Chinese tech giant Baidu just released its answer to ChatGPT.

By Melissa Heikkiläarchive page

March 21, 2023   STEPHANIE ARNETT/MITTR | GETTY

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

WOW, last week was intense. Several leading AI companies had major product releases. Google said it was giving developers access to its AI language models, and AI startup Anthropic unveiled its AI assistant Claude. But one announcement outshined them all: OpenAI’s new multimodal large language model, GPT-4. My colleague William Douglas Heaven got an exclusive preview. Read about his initial impressions.  

Unlike OpenAI’s viral hit ChatGPT, which is freely accessible to the general public, GPT-4 is currently accessible only to developers. It’s still early days for the tech, and it’ll take a while for it to feed through into new products and services. Still, people are already testing its capabilities out in the open. Here are my top picks of the fun ways they’re doing that.

Hustling

In an example that went viral on Twitter, Jackson Greathouse Fall, a brand designer, asked GPT-4 to make as much money as possible with an initial budget of $100. Fall said he acted as a “human liaison” and bought anything the computer program told him to. 

GPT-4 suggested he set up an affiliate marketing site to make money by promoting links to other products (in this instance, eco-friendly ones). Fall then asked GPT-4 to come up with prompts that would allow him to create a logo using OpenAI image-generating AI system DALL-E 2. Fall also asked GPT-4 to generate content and allocate money for social media advertising. 

The stunt attracted lots of attention from people on social media wanting to invest in his GPT-4-inspired marketing business, and Fall ended up with $1,378.84 cash on hand. This is obviously a publicity stunt, but it’s also a cool example of how the AI system can be used to help people come up with ideas. 

Productivity

Big tech companies really want you to use AI at work. This is probably the way most people will experience and play around with the new technology. Microsoft wants you to use GPT-4 in its Office suite to summarize documents and help with PowerPoint presentations—just as we predicted in January, which already seems like eons ago. 

Not so coincidentally, Google announced it will embed similar AI tech in its office products, including Google Docs and Gmail. That will help people draft emails, proofread texts, and generate images for presentations.  

Health care

I spoke with Nikhil Buduma and Mike Ng, the cofounders of Ambience Health, which is funded by OpenAI. The startup uses GPT-4 to generate medical documentation based on provider-patient conversations. Their pitch is that it will alleviate doctors’ workloads by removing tedious bits of the job, such as data entry. 

Buduma says GPT-4 is much better at following instructions than its predecessors. But it’s still unclear how well it will fare in a domain like health care, where accuracy really matters. OpenAI says it has improved some of the flaws that AI language models are known to have, but GPT-4 is still not completely free of them. It makes stuff up and presents falsehoods confidently as facts. It’s still biased. That’s why the only way to deploy these models safely is to make sure human experts are steering them and correcting their mistakes, says Ng.

Writing code

Arvind Narayanan, a computer science professor at Princeton University, saysit took him less than 10 minutes to get GPT-4 to generate code that converts URLs to citations. 

Narayanan says he’s been testing AI tools for text generation, image generation, and code generation, and that he finds code generation to be the most useful application. “I think the benefit of LLM [large language model] code generation is both time saved and psychological,” he tweeted. 

In a demo, OpenAI cofounder Greg Brockman used GPT-4 to create a website based on a very simple image of a design he drew on a napkin. As Narayanan points out, this is exactly where the power of these AI systems lies: automating mundane, low-stakes, yet time-consuming task... ' 

Back to Synthetic Data

Have not talked this for sometime, still interesting.

What is Synthetic Data? The Good, the Bad, and the Ugly

Sharing data can often enable compelling applications and analytics. However, more often than not, valuable datasets contain information of sensitive nature, and thus sharing them can endanger the privacy of users and organizations.

A possible alternative gaining momentum in the research community is to share synthetic data instead. The idea is to release artificially generated datasets that resemble the actual data — more precisely, having similar statistical properties.

So how do you generate synthetic data? What is that useful for? What are the benefits and the risks? What are the fundamental limitations and the open research questions that remain unanswered?

All right, let’s go!

How To Safely Release Data?

Before discussing synthetic data, let’s first consider the “alternatives.”

Anonymization: Theoretically, one could remove personally identifiable information before sharing it. However, in practice, anonymization fails to provide realistic privacy guarantees because a malevolent actor often has auxiliary information that allows them to re-identify anonymized data. For example, when Netflix de-identified movie rankings (as part of a challenge seeking better recommendation systems), Arvind Narayanan and Vitaly Shmatikov de-anonymized a large chunk by cross-referencing them with public information on IMDb.

Aggregation Another approach is to share aggregate statistics about a dataset. For example, telcos can provide statistics about how many people are in some specific locations at a given time — e.g., to assess footfall and decide where one should open a new store. However, this is often ineffective too, as the aggregates can still help an adversary learn something about specific individuals.

Differential Privacy: More promising attempts come from providing access to statistics obtained from the data while adding noise to the queries’ response, guaranteeing differential privacy. However, this approach generally lowers the dataset’s utility, especially on high-dimensional data. Additionally, allowing unlimited non-trivial queries on a dataset can reveal the whole dataset, so this approach needs to keep track of the privacy budget over time.   ... '

Researchers Develop Tool to Identify Existing Drugs to Use in Future Outbreak

More Sim advances link existing drugs to future outbreaks.

Researchers Develop Tool to Identify Existing Drugs to Use in Future Outbreak

By New York University, March 9, 2023

“Drug repurposing strategies provide an attractive and effective approach for quickly targeting potential new interventions,” said Bud Mishra, a professor at NYU’s Courant Institute of Mathematical Sciences.

An artificial intelligence algorithm developed by a global team led by researchers at New York University (NYU) can identify existing drugs that could be repurposed during future pandemics.

The PHENotype SIMulator (PHENSIM) simulates tissue-specific infection of SARS-CoV-2 host cells and calculates the antiviral effects of existing drugs via in silico experiments that take into consideration selected cells, cell lines, and tissues under various alterations of biomolecules.

The tool's effectiveness in identifying drugs for repurposing was confirmed by comparing its results with recent in vitro studies.

NYU's Naomi Maria said, "We've been able to model the SARS-CoV-2 infection and identify several COVID-19 drugs currently available as potentially effective in battling the next outbreak."

Added NYU's Bud Mishra, "Identifying and selecting ahead of time the best candidates, prior to costly and laborious in vitro and in vivo experiments and ensuing clinical trials, could significantly improve disease-specific drug development."

From New York University 

View Full Article

SAS on New Analytics

Thoughtful intro to piece via SAS

4 ways you might not realize advanced analytics is changing the world

by LEXI REGALADO on JANUARY 24, 2023 

The word innovation often draws to mind images of self-driving cars, new phones, and shiny tech. Yet, innovation often happens behind the scenes, especially in advanced analytics.

Around the world, industries like healthcare, government, banking, manufacturing, and more rely on the latest advancements in analytics.

At SAS Explore, an event for technologists, Udo Sglavo, Vice President of Advanced Analytics Research and Development, shared four key areas of innovation happening at SAS.

Throughout the general session on day two at SAS Explore, Sglavo interviewed various experts about how SAS is paving the way in advanced analytics and machine learning. Together, they covered the speed and repeatability of advanced analytics, proactively preventing biased decisions in AI, analytics on the go, and the possibilities of synthetic data.

Making advanced analytics faster and more productive  

In the past, advanced analytics was limited to large-scale, high-dollar projects. With advancements made in the last decade and digitalization's ongoing impact in response to the pandemic, adoption has skyrocketed. Businesses now regularly use advanced analytics for decision making, demand planning, and more. Thankfully, analytics in the cloud helps to meet demand.

The speed and agility of SAS® Viya® 4 in the cloud allow data scientists to test multiple solutions faster and more productively. 

DIVE DEEPER: Watch this full demo with Josh Griffin, who heads the Advanced Analytics Foundation Department team, to learn more.   ... ' 

AI: A Taxonomy of Machine Learning and Deep Learning Algorithms

Once again an excellent post by Ajit Jaokar:   Thanks Ajit!

Below is just the intro overview, the much longer post comes through when you click through to Linkedin.   Nicely done, incudes as part of the taxonomy a number of typical usage descriptions.

Artificial Intelligence #5 : A taxonomy of machine learning and deep learning algorithms

Published on May 25, 2021   By Ajit Jaokar

Course Director: Artificial Intelligence: Cloud and Edge Implementations - University of Oxford

Like the Glossary I posted last week, there is no taxonomy for machine learning and deep learning algorithms.

Most ML/DL problems are classification problems, and a small subset of algorithms can be used to solve most of them (ex: SVM. Logistic regression or Xgboost). In that sense, a full taxonomy maybe an overkill. However, if you really want to understand something, you need to know acquire knowledge of a repertoire of algorithms – to overcome the known unknowns problem.

In this post, rather than present a taxonomy, I present a range of taxonomy approaches for machine learning and deep learning algorithms. Some of these are mathematical. If you are just beginning data science, start from the non-mathematical approaches to taxonomy. Don't be tempted to go for the maths approach. But if you have an aptitude towards maths, you should consider the maths approach because it gives you a deeper understanding. Also, I am a bit biased because many in my network in Oxford, MIT, Cambridge, Technion etc would also take a similar maths-based approach.

Finally, I suggest one specific approach to taxonomy which I like and find most complete. It is complex but it is free to download.

Taxonomy approaches

Firstly, the approach from Jason Brownlee is always a good place to start because its pragmatic and implementable in code in A tour of machine learning algorithms. Note that these are machine learning algorithms (not deep learning algorithms). A more visual approach is below source packt.  .... " 

AI and IOT Use Cases

 Via link from O'Reilly.   Obvious perhaps but work thinking through the reasons why again.  As noted security is a particular concern for IOT.

AI and IoT – 5 use cases where it’s gathering pace  in TechHQ

While IoT sensors detect external information, replacing it with a signal that humans and machines can distinguish, it’s AI that helps to build intelligent machines.

The convergence of AI (Artificial Intelligence) and IoT (Internet of Things) unlocks a huge potential for businesses worldwide.

While IoT sensors detect external information, replacing it with a signal that humans and machines can distinguish, it’s AI that helps to build intelligent machines that learn from that data to support the decision-making process with little or no human interference.

Humidity and temperature sensor prototype at school for IoT device. Weather box IoT DIY in education campus

5 steps to making IoT networks more secure

The use of IoT is surging, and by the end of this year it’s predicted there could be up to 50 billion connected devices. Married with AI, this tide of new technology could usher in new opportunities, changing the way entire industries operate.

To illustrate the potential, we’ve put together a whistlestop of ten emerging applications of AI-enabled IoT. .... '

Using AI to Find New Uses for Existing Medications

As I have observed, this is a common thing.   Making it faster or more precise could be very useful.

Using AI to Find New Uses for Existing Medications    By Ohio State University, January 8, 2021

Using artificial intelligence could help speed up the process of finding new uses for existing drugs.

Ohio State University (OSU) researchers used artificial intelligence to process massive datasets in order to determine whether existing drugs could be applied to illnesses for which they were not previously used.

The researchers used insurance claims data on roughly 1.2 million heart-disease patients, which included information on assigned treatments, disease outcomes, and various values for potential confounders (something other than the thing being studied that could be causing the results seen).

The study, which focused on repurposing medications to prevent heart failure and stroke in patients with coronary artery disease, identified nine drugs considered likely to provide those therapeutic benefits.

OSU's Ping Zhang said the model used in this study “could be applied to any disease, if you can define the disease outcome."

From Ohio State University

Google Tutorial and Presentation on AI and Machine Learning

Link via DSC to a considerable presentation on Machine Learning from Google. Below a quick intro with a clue to some common uses of what we are now calling AI.

Google Tutorial on Machine Learning
Posted by Capri Granville  in DSC

This presentation was posted by Jason Mayes, senior creative engineer at Google, and was shared by many data scientists on social networks. Chances are that you might have seen it already. Below are a few of the slides. The presentation provides a list of machine learning algorithms and applications, in very simple words. It also explain the differences between AI, ML and DL (deep learning.)  ...

              ......

Much, much more at the link above.   Really worth it if only to scan the slides ...

You can check out the whole presentation (96 slides) here.     ....   I see this is perhaps dated being from 2017,  but appears to be very useful and mostly non-technical. 

McKinsey: Where AI is Being Used

In the recent article in McKinsey:

AI Adoption Advances, but Foundational Barriers Remain

Survey respondents report the rapid adoption of AI and expect only a minimal effect on head count. Yet few companies have in place the foundational building blocks that enable AI to generate value at scale. ....

 .... Where AI is being used:

By sector, telecom, high-tech, and financial-services firms are leading the way in overall adoption. That said, looking across sectors and functions, the results suggest that companies are generally following the money when deploying AI, which seems to be gaining the most traction in the areas of the business that create the most value within a given industry (Exhibit 1). In retail, for example, the use of AI in marketing and sales processes is most common: 52 percent of retail respondents say they are using AI in marketing and sales, compared with 29 percent of all respondents.  .... 

Exhibit 1   ...   ( Below a sample see the complete chart at the link)  ... 

Real World Applications of Drones

Overlook of current uses and future applications.

Real-World Applications for Drones    By Logan Kugler
Communications of the ACM, November 2019, Vol. 62 No. 11, Pages 19-21
10.1145/3360911

 In June, Amazon announced it was close to being able to offer for package deliveries by drone for its Prime Air service. That same month, Uber said it plans to test food delivery by aerial drone in crowded cities. And drone delivery company Flytrex already touts the ability to deliver drinks via unmanned vehicle on the golf course.

Despite such announcements, drones are not crowding the skies over major cities and population centers just yet. But that may be about to change.

After several years of hype, widespread drone usage may be close to ready for primetime.

Drones increasingly are being deployed in a number of compelling real-world use cases. These use cases have drone companies and enthusiasts bullish that, no matter what happens, there are serious real-world applications for drone technology today and in the near future that will disrupt life and business as we know it.

Drone-Assisted Photography/Surveying
"Traditionally, we've seen drones being used for photography and surveying," says Eric Peck, CEO of Swoop Aero, an Australian company that delivers medical supplies via aerial drone. "It's all about data capture, because data really is driving the ability to generate economic growth at the moment."

From construction to insurance to real estate to agriculture, the ability to survey and photograph wide swaths of land and hard-to-reach locations with aerial drones is valuable to companies. For instance, high-quality photos and videos from different aerial angles can better showcase residential properties up for sale, more effectively highlighting elements that appeal to buyers.

Aerial footage shot by drones is less expensive than manually taking aerial footage from a helicopter. One drone photographer interviewed by The Baltimore Sun noted the cost differences: "I can drive up to my destination, plug my equipment in, and be done [photographing] in five or 10 minutes," said Jack Hardway, owner of a drone photography firm. "It doesn't cost me $5,000. It costs me pennies to put that thing in the air."

The cost is one benefit. The ability to collect more visual data from more angles than from a traditional camera also is important.

A Santa Monica, CA-based company called DroneBase uses unmanned aerial vehicles (UAVs, or aerial drones) to offer, among other services, aerial surveying of building rooftops to give insurance companies an easy way to assess damage related to claims. For insurance and surveying purposes, aerial drones offer the ability to cover more ground while traversing more areas and angles than might be possible (or affordable) with traditional manned aircraft.

Other use cases include surveying and monitoring progress at construction sites, and performing simple regulatory inspections for commercial real estate properties. Aerial drones are even used to fly around warehouses and find supplies or products faster and more accurately than humans do.

Aerial drones also come in handy in agricultural applications. They offer a dual benefit in this context. First, drones are used to survey fields. Instead of having to traverse hundreds or thousands of acres on foot or by vehicle, farmers have the ability to fly drones faster and more efficiently over large areas. That helps reduce the time it takes to monitor fields, as well as reducing the amount of fertilizer and pesticides they must use to maintain crops.

"We identify diseases and pests and fungus and weeds in the crop at an earlier stage," U.K. farmer Colin Rayner told German broadcaster DW. Some drones are even used to spray fields with pesticides. According to DW, Chinese drone company DJI sold 20,000 pesticide-spraying drones in 2018 alone.  .... " 

Use of AI in Retail

Useful to see how big retail is thinking this.

BBQ Guys and Lowe’s discuss best practices for implementing AI tech by Guest contributor   Bryan Wassel, Associate Editor, Retail TouchPoints

 ... Through a special arrangement, presented here for discussion is a summary of a current article from the Retail TouchPoints website. ... 

Fine-tuning data science solutions to optimize results has been, relatively speaking, the easy part. Preparing people throughout the retail organization to take advantage of the new insights is the more complicated task, IT executives indicated on a panel at the 2019 Retail Innovation Conference.

“Executives like to believe that 99 percent of your time is spent on building the algorithms involved — but actually that’s the smallest part,” said Doug Jennings, VP of data and analytics at Lowe’s.

Teams across the organization must be educated on how these solutions will affect their jobs and have reasonable expectations about how much things will change. “We have to show some sort of roadmap of where we want to go,” said Jason Stutes, director of analytics & design at BBQ Guys.

One key ingredient is making a dashboard that is able to go through insights piece by piece, enabling marketers to understand the popularity of items beyond just how many were sold. A carefully built machine learning tool helps Lowe’s pull apart historical sales at a very granular level to see just what shoppers are looking for in any given category. Taking into account activities at nearby competing retailers can be invaluable. ... " 

Why Wikipedia

Have had a long time interest in how we curate the world's knowledge.  And thus Wikipedia asks ...

Wikipedia Asks its Readers Why. 
Wikipedia has a lot of readers–approximately 6,000 people visit every second. Since the online encyclopedia isn’t your typical information source, it had never bothered to ask its users why they showed up, and what they wanted from the site.

That changed in June 2017, when Wikipedia asked readers in 14 languages one simple question: “Why are you reading this article today?” More than 215,000 responses flooded in, and here’s the first results of that data ... "