What to Expect in AI in 2023

Where this is being exercised the most, lots of testing in context now under way.  Where it is most valuable still being determined.     Many investments still needed,  though lots of free platforms out there for exploration,  time investment still  large.    Risk of use application still not  clear.

What to Expect in 2023, A Data Scientists Top 5 AI Predictions  in OpenDataScience

AI has come a long way in recent years, and it shows no signs of slowing down. In fact, many experts believe that we are on the cusp of some major breakthroughs in the field of artificial intelligence. With that in mind, here are my top five AI predictions for 2023:

1. Improved natural language processing: Natural language processing (NLP) is the ability of a computer to understand, interpret, and generate human language. This is a key area of AI research, and it has come a long way in recent years. In 2023, we can expect to see even more progress in this area, with AI systems becoming more adept at understanding and generating human language. This could have a major impact on a wide range of industries, including customer service, education, and healthcare.

[Want more on NLP? Download this e-book]

2. Increased use of AI in healthcare: AI has the potential to revolutionize the healthcare industry in several ways. In 2023, we can expect to see an increased use of AI in healthcare, with machine learning algorithms being used to predict patient outcomes, diagnose diseases, and even assist in surgical procedures. This could greatly improve the efficiency and accuracy of healthcare delivery, ultimately leading to better patient outcomes.

3. More intelligent and autonomous robots: Robotics and AI go hand in hand, and we can expect to see more intelligent and autonomous robots in the coming years. In 2023, we may see robots that are capable of performing a wider range of tasks, as well as ones that are able to adapt to new environments and learn from their experiences. This could have a major impact on industries such as manufacturing and logistics, where robots are already being used to perform various tasks.

4. Increased use of AI in finance: The finance industry has already started to embrace AI, and this trend is only set to continue in the coming years. In 2023, we can expect to see more use of AI in areas such as risk assessment, fraud detection, and investment management. This could lead to increased efficiency and accuracy in the finance industry, as well as potentially lower costs for consumers.

5. Greater adoption of AI in the workplace: AI has the potential to greatly impact the way we work, and we can expect to see an increased adoption of AI in the workplace in 2023. This could include the use of chatbots for customer service, machine learning algorithms for data analysis, and even the use of AI assistants to help with scheduling and other tasks. While there are certainly concerns about the potential impact of AI on employment, it is likely that the adoption of AI in the workplace will lead to the creation of new jobs and industries in the long run.

In conclusion, the future of AI looks bright, with many exciting developments on the horizon. From improved natural language processing to the increased use of AI in healthcare and the workplace, there are many reasons to be optimistic about the role that AI will play in our lives in the coming years. It will be interesting to see how these predictions play out and what other advancements we may see in the field of AI in 2023 and beyond. 

– Learn how SAS Viya can help you elevate your impact in data and AI.

– Read G2 reviews from your peers using SAS Viya. 

Iain Brown Ph.D.Iain Brown, PhD

Experienced data scientist, thought leader, blogger & lecturer. Passion for all things AI & Data Science

HVIDIA Funds UF For AI

As mentioned formerly, now with a impressive building.  A report from a former school of mine. Now with an impressive building: Malachowsky Hall for Data Science & Information Technology.  Funded by NVIDIA with future plans for a hub there. 

A name that has put UF on the AI map! 

With AI taking center stage in the curriculum across all colleges, it is only fitting that AI take center stage on campus as well. 

AI and other technologies will have a home in the heart of campus starting in 2023 when Malachowsky Hall for Data Science & Information Technology opens. The 263,000-square-foot building, anchored by a gift from Chris Malachowsky (BS ’80) and his company, NVIDIA, will create a hub for advances in computing, communications and cyber-technologies with the potential for profound societal impact. 

The building provides space for researchers and students in medicine, engineering, pharmacy, informatics and others to collaborate across disciplines. In making the gift, Malachowsky noted the possibility for AI and data sciences to be “life-changing” for students and scientists.

#GoGreater #GoGators #NamedSpaces #AIatUF

Low Code for Data Science

Low Code for Data Science

3 Reasons Why You Need Low-code Platforms For Data Science Solutions  in TowardsDataScience

Low-code ML applications help address the challenges of model maintenance, time-to-market, and talent shortage

Organizations across industries are turning to data and analytics to solve business challenges. A survey by New Vantage Partners found that 91 percent of enterprises have invested in AI. However, the same study found that just 26 percent of these firms have AI in widespread production.

Organizations are struggling to solve business challenges with AI. They find that building machine learning (ML) applications takes time and requires expensive maintenance and talent that’s in short supply. Leaders say that over 70% of data science projects report minimal or zero business impact.

Here’s how low-code ML platforms can help tackle these challenges.

What is low-code, and why this craze now?

Low-code is a software development approach that leverages a visual user interface to create applications instead of traditional hand-coding. For decades, developers built applications by writing thousands of lines of code from scratch, often round-the-clock.

Building software solutions using low-code falls somewhere in the continuum between programming from scratch and buying off-the-shelf. It brings the best of both worlds by balancing flexibility and time-to-market.

A low-code development platform (LCDP) is considered quicker to build, economical to maintain, and developer-friendly because of its visual approach.

Low-code tools empower enterprises by democratizing software development. Today, anyone with a business interest and basic technology skills can build an app using low-code technology. According to Gartner, by 2024, more than 65 percent of all app development will be on low code. Globally, the low-code market is projected to reach $187 billion by 2030.   ... ' 

Data Science Books

In KDNuggets:

Data Science books to start reading:   Good selection,   I would pick one or more that makes the most sense based on your current background and go from there. 

By Przemek Chojecki, CEO Contentyze

Data science is undoubtedly one of the hottest career choices right now. Companies (many of whom have data science departments) are hiring data scientists around the board. It is a considerable thing to become a data scientist. It is also a fantastic opportunity to hone your expertise if you are already a statistician and want to step through the ranks.

This article discusses the most popular data science books for any level.  .... '

Scaling AI

Good thoughts, intro below.

Scaling AI and data science – 10 smart ways to move from pilot to production

VB Staff by Venturebeat,  Presented by Intel

“Fantastic! How fast can we scale?” Perhaps you’ve been fortunate enough to hear or ask that question about a new AI project in your organization. Or maybe an initial AI initiative has already reached production, but others are needed — quickly.

At this key early stage of AI growth, enterprises and the industry face a bigger, related question: How do we scale our organizational ability to develop and deploy AI?  Business and technology leaders must ask: What’s needed to advance AI (and by extension, data science) beyond the “craft” stage, to large-scale production that is fast, reliable, and economical?

The answers  are crucial to realizing ROI, delivering on the vision of “AI everywhere”, and helping the technology mature and propagate over the next five years.  ... ' 

Unlocking Category & Brand Growth with Data Science

Late to announce this, included some people I know from MIT.   I attended

Web Event Reminder: Unlocking Category & Brand Growth with Data Science will be held 04/28/2021 at 11:00 AM EDT  ... 

[  This was a good presentation, will place the link to it here in a day or  two ]

If you have any issues accessing the event or have any questions, please contact Betty Dong at bdong@ensembleiq.com.  .... 

Via Consumer Goods Technology  CGT

Data Science Can only do so much in the face of a Pandemic

A look at data tech vs Covid,    What worked and what didn't.   But this, though provocative is a very early look.   Before we know exactly what happened.  Then repeat this study with much more data.    For now still premature.

Coalition of the Willing Takes Aim at COVID-19   Data Science can only do so much in the face of a Pandemic   By Chris Edwards in ACM

Communications of the ACM, January 2021, Vol. 64 No. 1, Pages 19-21  10.1145/3433952

The rapid spread of COVID-19 around the world during the first quarter of 2020 spurred a massive response across the technological base, not least in computer and data science. Scientists and technologists both inside and outside healthcare snapped into action as the scale of the outbreak became clear, some providing techniques they had been working on for years, others proposing new projects all aimed at arresting the virus' progress.

The European Molecular Biology Laboratory's Bioinformatics Institute (EMBL-EBI), for example, already had a multiyear project underway to build a portal for anonymized genetic data from patients. Rolf Apweiler, co-director of EMBL-EBI, says it became clear at an early stage in the pandemic that those who suffered the most serious symptoms were "not only old people with underlying health conditions, but relatively young and healthy people. It is unclear why they are vulnerable and it may be in their genetic makeup. Understanding that is pretty important because if we want to go back to normal life, we want to find people who are vulnerable and need more protection."

According to Apweiler, what would normally take several years was compressed to a matter of months. By mid-April 2020, the group had opened an early implementation of the portal.

Before the pandemic got underway, warnings about a new epidemic came from data mining systems already in place. Social media technology provided the earliest clues to scientists working outside China, when Canadian company BlueDot and two research groups independently registered online chatter about a pneumonia-like disease at the end of December 2019. In internal reports, Chinese authorities had noted the existence of a novel virus-borne disease only a few days beforehand.  .... ' 

Thinking about Approaching Tidy Data

Below an intro on the concept.   We laid out and used similar ideas, this organizes it well.  First stated by Hadley Wickham in his paper.    Hard to fully achieve because of context, but very useful. 

What is Tidy Data?  

A must-know concept for Data Scientists.   Outline by Benedict Neo   in Towards Data Science

Introduction

There’s a popular saying in Data Science that goes like this — “Data Scientists spend up to 80% of the time on data cleaning and 20 percent of their time on actual data analysis”. The origin of this quote goes back to 2003, in Dasu and Johnson’s book, Exploratory Data Mining and Data Cleaning, and it still true to this day.

In a typical Data Science project, from importing your data to communicating your results, tidying your data is a crucial aspect in making your workflow more productive and efficient. ... 

The process of tidying data would thus create what’s known as tidy data, which is an ideal first formulated by Hadley Wickham in his paper. So my article will be largely a summarization or extracting the essence of the paper if you will.

What is Tidy Data?

From the paper, the definition given is:

Tidy datasets provide a standardized way to link the structure of a dataset (its physical layout) with its semantics (its meaning)   To break down this definition, you have to first understand what structure and semantics means. ..."

Last Year in AI, Analytics, Machine Learning and Data Science ....

Good end of the year piece from KDNuggets that was instructive.

AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2020 and Key Trends for 2021

Tags: 2021 Predictions, AI, Ajit Jaokar, Analytics, Brandon Rohrer, Daniel Tunkelang, Data Science, Deep Learning, Machine Learning, Pedro Domingos, Predictions, Research, Rosaria Silipo

2020 is finally coming to a close. While likely not to register as anyone's favorite year, 2020 did have some noteworthy advancements in our field, and 2021 promises some important key trends to look forward to. As has become a year-end tradition, our collection of experts have once again contributed their thoughts. Read on to find out more.

By Matthew Mayo, KDnuggets.

To the chagrin of absolutely no one, 2020 is finally drawing to a close. It has been a rollercoaster of a year, one defined almost exclusively by the COVID-19 pandemic. But other things have happened, including in the fields of AI, data science, and machine learning as well. To that end, it's time for KDnuggets annual year end expert analysis and predictions. This year we posed the question:

What were the main developments in AI, Data Science, Machine Learning Research in 2020 and what key trends do you see for 2021?

Last year's noted main developments and predictions included continued advancements in many research areas, NLP in particular. While there can be debate as to whether 2020's big NLP advancement was as formidable as some may have originally thought (or continue to think), there is no doubt that there was a continued and intense focus on NLP research in 2020. It should not be difficult to surmise that this continues into 2021 as well.  ... "

Platforms for Data Science

Good piece in O'Reilly about this question,  largely-non technical and useful thoughts. Passing it on ...

Why Best-of-Breed is a Better Choice than All-in-One Platforms for Data Science
All-in-one platforms built from open source software make it easy to perform certain workflows, but make it hard to explore and grow beyond those boundaries.

By Matthew Rocklin and Hugo Bowne-Anderson,  in O' Reilly 

Do you buy a solution from a big integration company like IBM, Cloudera, or Amazon?  Do you engage many small startups, each focused on one part of the problem?  A little of both?  We see trends shifting towards focused best-of-breed platforms. That is, products that are laser-focused on one aspect of the data science and machine learning workflows, in contrast to all-in-one platforms that attempt to solve the entire space of data workflows.

This article, which examines this shift in more depth, is an opinionated result of countless conversations with data scientists about their needs in modern data science workflows.  ... " 

(Much more at the link) 

Introductory Guide to the Use of the Data Warehouse

Hardy comprehensive, as the title suggests, but a good introductory piece on the data warehouse.  A minimum you should know when dealing with corporate data.  Non-technical.

Comprehensive Guide to the Data Warehouse
Data science can’t start until the data cleaning process is complete. Learn about the role of the data warehouse as a repository of analysis-ready datasets.
Nicole Janeway Bills

As a data scientist, it’s valuable to have some idea of fundamental data warehouse concepts. Most of the work we do involves adding enterprise value on top of datasets that need to be clean and readily comprehensible. For a dataset to reach that stage of its lifecycle, it has already passed through many components of data architecture and, hopefully, many data quality filters. This is how we avoid the unfortunate situation wherein the data scientist ends up spending 80% of their time on data wrangling.  ... " 

Podcast: Do we Need Data Scientists in a World of Automation?

New Podcast below, by SAS and DSC.    Good topic.  The question should be:  How many and how should the be involved in an enterprise?  Just like Computer Scientists, Economists, Statisticians ... and other technical fields.  Machine Learning can't do it all.

Do We Need Data Scientists in Today’s World of Automation?

Machine learning is said to be an important driver of the future of intelligent systems, automatically analyzing data and distilling new knowledge, actionable insights and compelling decisions. But why show market trends that investments in data scientists – those golden people that train machine learning models – have never been higher if machine learning can be fully automated? Why do we need data scientists if machine learning is designed to do it all?

In today’s Data Science Central podcast, Véronique Van Vlasselaer, Data & Decision Scientist at SAS, will discuss what machine learning automation entails, and how valuable human input in the machine learning process is.

Speaker: Véronique Van Vlasselaer, Data & Decision Scientist - SAS
Hosted by: Rafael Knuth, Contributing Editor - Data Science Central

Like People, AI will also Fail.

A very nicely done, non-technical and usefully skeptical view of AI. Making the case that even if your AI solves a problem today, it is likely to fail tomorrow, when time and context drift and shift.  Just like human problem solvers can fail to find solutions to all problems.   In our own progress in the space, we engaged many of these experiences.   Sometimes we had to wait for decades to get better solutions.     I say know the risk and understand it too is shifting.  Build to solve useful problems.  Check your data and recheck your results.


What to Do When AI Fails  

By Andrew Burt and Patrick Hall in O'Reilly

These are unprecedented times, at least by information age standards. Much of the U.S. economy has ground to a halt, and social norms about our data and our privacy have been thrown out the window throughout much of the world. Moreover, things seem likely to keep changing until a vaccine or effective treatment for COVID-19 becomes available. All this change could wreak havoc on artificial intelligence (AI) systems. Garbage in, garbage out still holds in 2020. The most common types of AI systems are still only as good as their training data. If there’s no historical data that mirrors our current situation, we can expect our AI systems to falter, if not fail. 

To date, at least 1,200 reports of AI incidents have been recorded in various public and research databases. That means that now is the time to start planning for AI incident response, or how organizations react when things go wrong with their AI systems. While incident response is a field that’s well developed in the traditional cybersecurity world, it has no clear analogue in the world of AI.  What is an incident when it comes to an AI system? When does AI create liability that organizations need to respond to? This article answers these questions, based on our combined experience as both a lawyer and a data scientist responding to cybersecurity incidents, crafting legal frameworks to manage the risks of AI, and building sophisticated interpretable models to mitigate risk. Our aim is to help explain when and why AI creates liability for the organizations that employ it, and to outline how organizations should react when their AI causes major problems.  ... " 

All Models are Wrong, some Useful? Numbers are Suspect Too.

Based on the classic statement, that I often gave to execs seeking my help.    Most often about the forecast, which they were too quick to accept.

All numbers are made up, some are useful
Keeping track of stuff is hard
By Vicki Boykis in NormCore Tech  .... '

Some good and considerable thoughts in the linked-to blog/newsletter about data, measurement, statistics and even about the coronavirus ....   Worth a look ....

Gartner Magic Quadrant for Data Science and Machine Learning

KD Nuggets publishes and analyzes the most recent Gartner quadrant analysis. While I am skeptical of this approach, it does have a useful list of participants which can fill in the gaps.   Clip at link below to get to the 'Magic Quadrant'.   Some of the included analysis by KDN is more interesting, with  short, general, non-technical descriptions of what many companies are doing.

The Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms has the largest number of leaders ever. We examine the leaders and changes and trends vs previous years.
By Gregory Piatetsky, KDnuggets.

Gartner has released last week its highly-anticipated report and magic quadrant (MQ) for Data Science and Machine Learning Platforms (DSML) and you can get copies from several vendors - see a list at the bottom of this blog. In previous years, the MQ name kept changing but the 4 leaders remained the same. Now the name has remained the same as in 2019 MQ and 2018 MQ reports, reflecting a more mature understanding of the DSML field, but the contents, especially the leader quadrant, have changed dramatically, reflecting accelerating progress and competition in the field.

The 2020 MQ report went back to evaluating 16 vendors (down from 17 last year), placed as usual in 4 quadrants, based on completeness of vision (vision for short) and ability to execute (ability for short).

We note that the report included only vendors with commercial products, and did not consider open-source platforms like Python and R, even though those are very popular with Data Scientists and Machine Learning professionals.   ... )

Using Business Rules and Expertise

Via DSC, what looks to be a good podcast on this topic.  It has been a favorite approach of mine since the beginning.  Narrow machine learning methods can be very valuable, but to deliver them they have to be part of existing or proposed tasks or businesses.  Operationally embedded.   That requires real-life decision rules.  Access information to the podcast at the link below.  More on this topic to follow. 

Data Science Fails: Ignoring Business Rules & Expertise 

Nowadays, we have unprecedented access to data, plus the computing power and advanced algorithms to find correlations. We look at a cautionary case study of a cancer center that embarked on an ambitious plan to use AI to eradicate cancer. When AI is being asked to make decisions with significant consequences, such as life and death healthcare recommendations, it needs to be trustworthy. But if you don't follow best practices, if you don't include the knowledge of subject matter experts, and if you don't enforce business rules, your AI project will not be successful.

In this latest Data Science Central podcast, learn four AI governance practices that can help you achieve AI success.

Speaker: Colin Priest, VP of AI Strategy - DataRobot
Hosted by: Sean Welch, Host and Producer - Data Science Central .... 

Data Science in Film Industry

Fairly generalized thoughts, but an areas which is underdeveloped. 

How Data Science Is Used Within the Film Industry

As Data Science is becoming pervasive across so many industries, Hollywood is certainly not being left behind. Learn about how Big Data, analytics, and AI are now core drivers of the movies we watch and how we watch them. 

By Frankie Wallace. in KDNuggets

There are countless factors at play in filmmaking, from determining production costs to developing targeted marketing campaigns. Data science is involved in practically every step of the process, and professionals who work in data science can learn many things from the film industry.

Streaming services are at the forefront of the data science revolution. Production companies, including Amazon, Hulu, and Netflix, analyze patterns in big data to determine the types of content they create and make personalized viewing recommendations. In this way, data science can aid the art of producing and marketing entertainment at levels never before seen.

The field of data science also pops up as meaty subject matter in a variety of films. The stories of real-life innovators such as Alan Turing and John Nash have been turned into major films in recent years, living alongside fictionalized tales that use predictive analysis, machine learning, and AI as central plot themes. .... "  ...'

IBM Has Successful AI Kickstarting Effort

Worked on several IBM initiated projects, was unaware of this particular effort.  A little unlcear how success is measured.  Though they say data science at first later AI is included.  Good general approach, which is not much different than many other IBM 'top down' efforts.

IBM’s ‘elite’ data science squad has kickstarted AI for more than 100 companies
By   Matt Marshall

Last year, IBM announced a Data Science Elite team whose only job is to help big enterprise companies push their first AI models into production.

Now, more than a year after the program’s launch, Rob Thomas, the IBM executive overseeing the AI SWAT team, reports that it has been a “huge success.” The team has increased from 30 data scientists to 100, and there are plans to grow significantly next year. “We hire them wherever we can, actually,” Thomas said, noting that these data scientists operate all over the world. (Thomas told VentureBeat about this elite team as part of a wider-ranging interview.)  .... " 

On the State of AI and Machine Learning

Brought to my attention, a short non technical document on the topic.

The State of AI and Machine Learning    32 pages

From Figure Eight
Bridging the AI Gap Between Data Scientists and Line-of-Business Owners

01  Introduction
02  About the Survey
03  Why are Data Scientist Not 100% Satisfied in Their Jobs?
04  The Future is … Human? Machine? Cyborg?
05  Line of Business Budgets Suggest
06  Growing Importance of AI Initiatives Bridging the AI Gap
07  Crawl, Walk, Run with AI
08  Conclusion
09  References
...." 

Considering Unforseen Circumstances

Are always an issue, and not considered carefully enough.  Surely should be talked about in any analytical effort. Was something we called 'Solution Context'.  Nice space by Peter Bruce that is worth following.    Many more examples at the link.

Unforeseen Consequences in Data Science    By: Peter Bruce

After the massive Exxon Valdez oil spill, states passed laws boosting the liability of tanker companies for future spills.  The result was not as intended: fly-by-night companies, whose bankruptcy would not be consequential, took over the trade. In this blog we look at some notable examples of unforeseen consequences of analytics algorithms.  ..."