Book: In the Age of AI, 'What Does it Mean to be Smart?'

 Here an intro to McKinsey's 'Author Talks', which interviews Tomas Chamorro-Premuzic on his new book on AI and Automation.   Plan to Read it. 

Author Talks: In the ‘age of AI,’ what does it mean to be smart?

March 16, 2023 | Interview

As artificial intelligence gets better at predicting human behavior, a business psychologist encourages people to strengthen the uniquely human skills that machine learning has yet to tap.

In this edition of Author Talks, McKinsey Global Publishing’s Raju Narisetti chats with Tomas Chamorro-Premuzic about his new book, I, Human: AI, Automation, and the Quest to Reclaim What Makes Us Unique (Harvard Business Review Press, February 2023). Chamorro-Premuzic explains why some AI algorithms model humanity as a simple species, how attention has become commoditized, and why the right questions are now more valuable than the right answers. An edited version of the conversation follows.

Why did you write this, your 12th book, now?

I’m a professor of business psychology at Columbia University and UCL [University College London] and the chief innovation officer at ManpowerGroup. I, Human: AI, Automation, and the Quest to Reclaim What Makes Us Unique is a book about the behavioral consequences or impact of artificial intelligence, including the dark side of human behavior and what we should do to upgrade ourselves as a species.

The book is written at a time that, in my view, could only be described as the AI age. Humans have always relied on technological inventiveness and innovation to shape their cultural and social evolution, and I think there can be very little doubt that the definitive technology of today is artificial intelligence, or AI.

Now, even the wider public is talking about things like ChatGPT and other conversational interfaces, and the tech giants are described mostly as data companies and as algorithmic prediction businesses.

The book was very much written in the midst of the AI age, or under the influence of AI, because I wrote the bulk of this at the height of the pandemic when we had very little physical interaction or contact with other people outside of our nuclear families. This means I was heavily influenced by hyperconnectedness and the datafication of me. Everything I did was being datafied and subjected to the predictive powers of AI during 2020 and 2021.

I can’t say that there won’t be a better era to read the book, but it certainly wouldn’t have had the same connotation and impact if we had published it five or ten years ago.

Haven’t humans always blamed technology for every problem they face?

There is a common tendency for people to overreact to things that are novel, whether in a good way or in a bad way, and technologies are a very good example of this.

Perhaps the best example is how, when the written newspaper first scaled up and productized, people feared that humans would never meet in person ever again because there would be no information or even gossip to exchange if all the news was in written form. Also, from the 1950s onward, people showed concern that television would lead to less intellectual activities, but I don’t think they were wrong because reading habits went down since mass TV was introduced.

What I tried to do with this book is not be at one extreme or the other. What’s important to me is to not miss the opportunity to highlight the behavioral impact and consequences that we have already seen artificial intelligence have on us. This is not a book about AI, but about humans in the AI age.  .. .. ' 

Automated Decision Making at the Warehouse

 Useful thoughts, makes much sense.

Automated Decision-Making in the Warehouse

Warehouse-within-warehouse pictured on tablet screen

Canva.com/yoh4nn

March 16, 2023  Sponsored by LogistiVIEW

Though warehouse management systems (WMS) are widely employed, they are not the ultimate answer to optimized warehouse operations. Moving to the next level — using automated intelligence for real-time decision-making and task-flow optimization — is now feasible.

While advanced process optimization has been employed in many manufacturing operations for some time, the added complexities of many warehouse operations have made this impractical until now.

In a manufacturing plant, there are typically one or more assembly lines that perform a limited number of tasks. In a warehouse — particularly in those handling e-commerce or consumer goods — an assembly line has to be essentially created for each of the hundreds or thousands of items being handled in real time, with the product mix continually changing.

Today, WMS requires people at all levels in the operation to make decisions based on complex information. Its capabilities are designed to ensure that data and inventory are not lost, providing detailed input, but not a clear story, of how that information should influence the next decision. Only now has computing power advanced to the level at which AI can successfully take over where manual decision-making has typically been required in most WMS systems.

Relieving Pressures on Decision Makers

When there’s too much data for a human to process in real time, managers can be pushed to make decisions that are sometimes based on a “gut” feeling or on historical practice, in situations that seem similar to circumstances they’ve previously encountered.

In reality, a number of decisions need to be made almost simultaneously, including:

•    What product needs to be released next?

•    What path should the order take through the warehouse?

•    Who should work on the order?

•    How should a scheduling exception be handled?  .... ' 

Retraining, Essential.

Examined in practice. 

Published in  Towards Data Science,      By Claire Longo

Embracing Automated Retraining

How to move away from retraining at a set cadence (or not at all) in favor of a dynamic approach

This piece was co-authored by Trevor LaViale

While the industry has invested a lot in processes and techniques for knowing when to deploy a model into production, there is arguably less collective knowledge on the equally important task of knowing when to retrain a model. In truth, knowing when to retrain a model is hard due to factors like delays in feedback or labels for live predictions. In practice, many models are in production with no retraining at all, use manual retraining methods, or are retraining without optimizing or studying the cadence.

This post is written to help data scientists and machine learning engineering teams embrace automated retraining.

Approaches for Retraining

There are two core approaches to automated model retraining:

Fixed: Retraining a set cadence (e.g., daily, weekly, monthly)

Dynamic: Ad-hoc triggered retraining based on model performance metrics.

While the fixed approach is straightforward to implement, there are some drawbacks. Compute costs can be higher than necessary, and the frequent retraining can lead to inconsistencies from one model to another, while infrequent retraining schedules can lead to a stale model.

The dynamic approach can prevent models from going stale, and optimize the compute cost. While there are numerous approaches to retraining, here are some recommended best practices for dynamic model retraining that will keep models healthier and performant.

Generalized Retraining Architecture

Data Intelligence Platform Seek AI Launches to Automate Repetitive Tasks

 Automate and make intelligent. 

Data Intelligence Platform Seek AI Launches to Automate Repetitive Tasks

In DataNami  by Jaime Hampton

With all the hype around generative AI and its text-to-image capabilities throwing the art world for a loop, it may be easy to forget that you can write code with it, too.

Seek AI, a new data intelligence platform that uses generative AI, specifically large language models, has just been launched. The company is the creation of CEO Sarah Nagy, a data professional and entrepreneur who felt the available data tools were not adequate for helping her less technical colleagues access needed data. Nagy and co-founder Sarah Smith decided to look to these newest advances in AI for a solution.

“For anyone that has worked with generative AI like DALL-E, Stable Diffusion, or GPT-3, you have experienced the power of these models, which work like magic. But did you know that generative AI can also write code? When I first saw this in action, I knew instantly that it would change the way people would work with data,” Nagy said in a company mission statement.

Data access can be challenging for business users less familiar with coding, which can add hours of work to the already heavy workflows of the data professionals that assist them. As part of her data career, Nagy says she spent a significant amount of time each day writing manual code by hand to pull data for her colleagues.

Seek AI has launched its SaaS platform to automate these repetitive tasks. According to the company, Seek AI allows data teams to automate and oversee database query projects to improve productivity, with a focus on sales and marketing purposes.

“We’ve built a platform that uses artificial intelligence to reduce inefficiencies in accessing data,” says Nagy. “Currently, data scientists and business analysts waste time manually writing repetitive code. We’ve automated that process with sophisticated natural language processing and machine learning.”

The complex deep-learning models behind OpenAI’s DALL-E and GPT-3 are also behind the Seek AI platform’s technology and form the basis for its natural language interface that allows users to ask questions and obtain immediate answers. The models understand natural language commands and generate the necessary code for instantly querying databases.

Seek AI’s NLP interface transforms natural language questions into code for querying databases. Source: Seek AI

Seek AI’s natural language interface can be accessed through email, Slack, text, and several CRM systems. Available as a monthly or per user subscription, the company says its platform allows data teams to automate the generation and maintenance of code for answering ad-hoc questions, as well as generating and maintaining semantic models.

“By helping to remove some of the mundane coding and waiting from both the data team and the business-facing teams, I hope that Seek will make knowledge workers happier because they will be able to pursue more meaningful work. Not to mention the value it adds to organizations by allowing them to make better use of the data they work so hard to compile and maintain,” said Nagy.

“Giving fast, high-quality answers to our customers’ questions is the difference between winning and losing over our competitors,” said Tim Harrington, CEO of financial dataset supplier Battlefin, in a release. “Seek AI played a critical role in our company’s 2023 strategy because of the edge that it gives us in accessing and analyzing our 2,400+ datasets in response to customer questions. I’d estimate that our ROI on Seek AI is about 10x based on what we would have spent to achieve this level of efficiency without the platform.”  .... ' 

Amazon Launches Warehouse Robot That Can Do Human Jobs

ACM TECHNEWS

Amazon Launches Warehouse Robot That Can Do Human Jobs  By Financial Times, November 11, 2022

Amazon has rolled out a new warehouse robot in order to automate more jobs as the company seeks to reduce logistics costs.  The company said the Sparrow robotic arm is the first robot that can "detect, select, and handle individual products in our inventory," jobs once performed only by warehouse employees.  The robotic arm identifies and picks up small objects with the help of computer vision technology and suction cups.

Amazon, which has developed 700 new robotics-related job "categories," said Sparrow will "benefit" employees by allowing them to focus more on less-repetitive warehouse tasks.

Joe Quinlivan, Amazon’s vice-president of global robotics, wrote in a blog post, “Robotics technology enables us to work smarter — not harder — to operate efficiently and safely.” ... 

From Financial Times

View Full Article - 

Addressing Labor Shortages with Automation

Thoughts on topic, but how can they be applied?

Addressing Labor Shortages with Automation  By Logan Kugler

Communications of the ACM, June 2022, Vol. 65 No. 6, Pages 21-23 10.1145/3530687

U..S. employment statistics hit a new milestone last year, but not a positive one. In August 2021, almost 4.3 million workers quit their jobs, according to the U.S. Department of Labor. That's the highest number since the department began tracking voluntary resignations. Their reasons for leaving their jobs vary—the numbers track people who quit for a different position, as well as those who quit without having another job lined up.

While the reasons for quitting vary, one thing is clear: Businesses are having a tough time getting employees to come back. A full 80% of companies surveyed by the Conference Board, a business research nonprofit, say they are now finding it difficult to hire qualified workers.

This is a win for worker wages. Additional Conference Board research predicts U.S. wage costs for companies will rise 3.9% in 2022, the highest jump since 2008. In the U.K., the National Institute of Economic and Social Research expects average weekly earnings growth to jump 5.9% in 2021, compared with 1.8% in 2020. However, the growth in wages may not last.

The labor shortage, combined with wages increasing to try to attract new employees and keep those they have, have led many businesses to accelerate plans to adopt automation technologies like robots and smarter software. Such adoption is happening across industries like manufacturing, the service industry, and administrative work. It is unclear if this automation will alleviate a permanent shortage of workers who no longer want this type of work—or if it will permanently eliminate jobs that may be in future demand.

The recent surge in demand for automation started out as a temporary necessity, says Gad Levanon, vice president of Labor Markets at the Conference Board and founder of the organization's Labor Market Institute.

Many companies had to limit human interactions in order to comply with public health regulations during the pandemic. Using automation to take services online or rollout self-service options was the only way for some companies to keep doing business legally. However, as businesses reopen, many now see automation as a sensible permanent solution to the labor shortfall they now face.

"After massive layoffs during the early months of the pandemic, some have learned to operate with fewer workers by using more automation and other process improvements," says the Conference Board's Levanon says. "2021's severe labor shortage and accelerating wages may incentivize other employers to do the same."  .... '

Automating Data Science: Prospects and Challenges

 Long, insightful piece, good insights,  somewhat technical, but useful.   A favorite topic we experimented with since very early days.

Home/Magazine Archive/March 2022 (Vol. 65, No. 3)/Automating Data Science/Full Text

 By Tijl De Bie, Luc De Raedt, José Hernández-Orallo, Holger H. Hoos, Padhraic Smyth, Christopher K. I. Williams

Communications of the ACM, March 2022, Vol. 65 No. 3, Pages 76-87 10.1145/3495256

--> Given the complexity of typical data science projects and the associated demand for human expertise, automation has the potential to transform the data science process.

Key insights: 

• Automation in data science aims to facilitate and transform the work of data scientists, not to replace them.

• Important parts of data science are already being automated, especially in the modeling stages, where techniques such as automated machine learning (AutoML) are gaining traction.

• Other aspects are harder to automate, not only because of technological challenges, but because open ended and context-dependent tasks require human interaction.

Introduction

Data science covers the full spectrum of deriving insight from data, from initial data gathering and interpretation, via processing and engineering of data, and exploration and modeling, to eventually producing novel insights and decision support systems. Data science can be viewed as overlapping or broader in scope than other data-analytic methodological disciplines, such as statistics, machine learning, databases, or visualization

To illustrate the breadth of data science, consider, for example, the problem of recommending items (movies, books or other products) to customers. While the core of these applications can consist of algorithmic techniques such as matrix factorization, a deployed system will involve a much wider range of technological and human considerations. These range from scalable back-end transaction systems that retrieve customer and product data in real time, experimental design for evaluating system changes, causal analysis for understanding the effect of interventions, to the human factors and psychology that underliehow customers react to visual information displays and make decisions.

As another example, in areas such as astronomy, particle physics, and climate science, there is a rich tradition of building computational pipelines to support data-driven discovery and hypothesis testing. For instance, geoscientists use monthly global landcover maps based on satellite imagery at sub-kilometer resolutions to better understand how the earth’s surface is changing over time [50]. These maps are interactive and browsable, and they are the result of a complex data-processing pipeline, in which terabytes to petabytes of raw sensor and image data are transformed into databases of automatically detected and annotated objects and information. This type of pipeline involves many steps, in which human decisions and insight are critical, such as instrument calibration, removal of outliers, and classification of pixels.

The breadth and complexity of these and many other data science scenarios means that the modern data scientist requires broad knowledge and experience across a multitude of topics. Together with an increasing demand for data analysis skills, this has led to a shortage of trained data scientists with appropriate background and experience, and significant market competition for limited expertise. Considering this bottleneck, it is not surprising that there is increasing interest in automating parts, if not all, of the data science process. This desire and potential for automation is the focus of this article.

As illustrated in the examples above, data science is a complex process, driven by the character of the data being analyzed and by the questions being asked, and is often highly exploratory and iterative in nature. Domain context can play a key role in these exploratory steps, even in relatively well-defined processes such as predictive modeling (e.g., as characterized by CRISP-DM [5]) where, for example, human expertise in defining relevant predictor variables can be critical.  .... '

Business Process Analysis: When to Use

Yes, absolutely,  of anything significant, also for anything new or advanced like AI, or things that interact with people or have risky aspects.

Why a Business Process Analysis is critical before applying Automation

Simon Carter  in Towards Data Science

A Business Process Analysis is a powerful way to understand the health of various processes, and identify ways to improve their efficiency and effectiveness. In other words, it is about honest self-reflection to understand both what’s working, and what’s not.

Such an analysis enables process owners to understand the activities and people involved in the process, identify issues like delays, errors, or customer complaints, collect data about these issues, and then articulate plans and roadmaps for process improvements or enhancements.

It works best when applied to the right as-is process, because it can enable process owners to understand whether the process is meeting stated goals, and accordingly make sound judgements and decisions if it’s found to be sub-optimal. It’s also useful to use a Business Process Analysis before implementing any Automation to ensure that the process is both optimised and “automation-ready”.

However, there’s a common misconception among many companies, particularly those that are novices at such analyses and automation initiatives — that automation can help them magically “fix” broken processes.

It can’t.

This article explores why.

Business Process Analysis: Which Processes to Automate

Business Process Analysis tools such as flowcharts, process maps, gap analysis, and root cause analysis, along with automation technologies like Business Process Automation (BPA) and Robotic Process Automation (RPA) can help you find gaps and streamline many types of processes in areas like:  .... ' 

LIDAR on its Way Out?

Have been looking at smartphone loaded uses of LiDAR, So the question is useful.

Is LiDAR on its way out? The business case for saying goodbye

As automation proliferates, sensing is increasingly moving away from LiDAR.

Written by Greg Nichols, ContributorGreg Nichols

Among the deluge of robotics predictions you're bound to encounter this year, there's one you should pay particular attention to: The way robots "see" is fundamentally changing, and that's going to have a huge impact on the utility cost and proliferation of robotic systems.

Of course, it's a bit of a mischaracterization to talk about robots "seeing," or at least a reductive shorthand for a complex interplay of software and hardware that's allowing robots to do much more sophisticated sensing with much less costly equipment. Machine vision incorporates a variety of technologies and increasingly relies on software in the form of machine learning and AI to interpret and process data from 2D sensors that would have been unachievable even a short time ago.

With this increasing reliance on software comes an interesting shift away from highly specialized sensors like LiDAR, long a staple for robots operating in semi-structured and unstructured environments. Robotics experts marrying the relationship between humans and AI software are coming to find that LiDAR isn't actually necessary. Rather, machine vision is providing higher quality mapping\at a more affordable cost, especially when it comes to indoor robotics and automation.... ' 

Smart Devices and Automation in Supply Chain

Smart Devices and the Supply Chain

Ivanti links smart devices to bring automation to supply chain operations

BY MIKE WHEATLEY  in SilicaonAngle

Ivanti Software Inc.’s supply chain business unit, Ivanti Wavelink, said today it’s extending its Ivanti Neurons platform to help companies automate and optimize their supply chain operations to drive more efficiency.

With the new Ivanti Neurons for Industrial Internet of Things platform, enterprises will be able to build scalable applications that integrate into existing processes to drive operational efficiency, the company said.

The security-focused Ivanti Neurons platform was launched in 2020, giving organizations a way to automate the management of endpoints such as employee devices. It works by mapping all of the devices on a corporate network, scanning them for potential issues and then automatically resolving what vulnerabilities, performance and compliance issues it finds.

Ivanti Wavelink said it’s now applying that same concept to the supply chain, taking advantage of numerous smart devices, including smart conveyors, sensors, cameras and robots. With Ivanti Neurons for IIoT, available now, companies can create low-code or no-code applications to link these disparate smart devices with existing systems in order to automate and enhance their supply chain processes.

The idea is that Ivanti Neurons for IIoT can help organizations to overcome problems such as labor shortages, poor distribution management and a lack of visibility that impact their supply chains, through increased digitization. A customer might integrate Ivanti Neurons for IIoT with Ivanti Velocity using the MQTT protocol for IoT devices, for example. By doing so, that will help them to create more automation for existing task-worker processes without needing to invest in expensive warehouse management system changes. The platform can also generate more insights on those processes..... ' 

Developers are Better with Automation

 Will have to deal with this as automation progresses. How about the influence on say the security of the code being written?

Developers Are Better With Automation and Reusable

ACM CAREERS

Developers Are Better With Automation and Reusable Code, Report Says

By Tech Republic. November 18, 2021

Software teams are changing their coding processes to fit the new dynamics of remote work, according to GitHub's 2021 State of the Octoverse. That means reusing code, embracing automation, and getting better at documentation. The research combines telemetry from more than 4 million repositories and a survey of about 12,000 developers.

Automating software delivery is important to open source work and helps teams go faster at scale, the research found. Teams that use Actions "merge almost 2x more pull requests per day than before (61% increase) and they merge 31% faster," the report says. Automation helps teams communicate better and more clearly, which also helps build a better culture, according to the report.

Reuse is another key to make the development process go faster with performance increasing up to 87%. Reuse also helps open source projects with double the performance improvement compared to processes that are slow or have multiple approval layers.

Investing in documentation was found to have a direct impact on productivity. The research found that documentation gives developers a 50% increase in productivity.

From Tech Republic

View Full Article    

McD Drive-Through Efficiency with Voice Recognition

Have been following the use of drive-through and related efficient consumer interactions in the Pandemic.

IBM, McDonald's to serve up automated drive-thru lanes with strategic partnership

Under the agreement, IBM will acquire McD Tech Labs, which has been working to develop, test and deploy an automated ordering system using artificial intelligence-enabled voice recognition .... in Fox Business  .. '

Retail Hyperautomation?

My friend Gib Basset authors this piece in Retailwire, including further discussion: 

Will ‘hyperautomation’ determine retailing success from this point forward?

by Gib Bassett in Retailwire

There is no doubt 2021 is shaping up to be memorable for retailers and consumers alike. It’s not the happy kind of memory either, with once-in-a-lifetime disruptions rippling through supply chains causing costly delays, shortages and capacity problems that highlight a lack of collaboration and visibility among stakeholders.

That’s a big nut to crack that will take both time and a hearty constitution to overcome, so what can be done now to stem the losses?

The success of functions including sales, operations, procurement and shipping depends on retailers’ ability to apply analytics at scale towards developing better insights and responses to consumer demands.

With stress at a fever pitch heading into the holidays, retailers are bolstering nascent last-mile, omnichannel capabilities such as buy online and pickup curbside or in-store, plus more timely delivery options like intra-day. All of these align with front-office, CRM-led investments focused on customer experience.

Some make the mistake of separately pursuing improvements in back-office supply chain functions, like demand planning, procurement, transportation and shipping. Consumers minimally expect that inventory presented online syncs with what’s available in-store. That’s table stakes.

Weaving everything — front to back — together with better automation, analytics and data offers a near-term path with long-term legs. The digital transformations that began last year will require further transformations as course correction becomes a matter of survival.

The term “hyperautomation” may sound fantastical, but its implications promise improvements to supply chains that will come to define successful retail in 2022 and beyond.

Gartner named hyperautomation, which blends machine learning, artificial intelligence (AI) and robotic process automation (RPA) and artificial intelligence (AI), as the top supply chain innovation of 2021.

“The key principle of hyperautomation is that everything that can be automated will be automated,” according to the report.  .... '

Now we are Cooking with Lasers

 A long time amateur chef

Now We're Cooking With Lasers   By Columbia Engineering, September 20, 2021

Researchers at Columbia Engineering have digitized food creation and cooking processes, using 3D printing technology to tailor food shape and texture and lasers of various wavelengths to cook it.

"Precision Cooking for Printed Foods via Multiwavelength Lasers,"    Science of Food, explores various modalities of cooking. The researchers printed chicken samples as a test bed and assessed a range of parameters. They found that blue lasers (445 nm) are best for penetrative cooking, and infrared lasers (980 nm and 10.6 μm) best for browning. Laser-cooked meat shrinks 50% less and retains double the moisture as meat cooked in conventional ovens.

"Our two blind taste-testers preferred laser-cooked meat to the conventionally cooked samples, which shows promise for this burgeoning technology," says Jonathan Blutinger, a Ph.D. student in the Creative Machines Lab at Columbia University.

 "What we still don't have is what we call 'Food CAD,' sort of the Photoshop of food," says Professor Hod Lipson, lead author of the study. "We need a high level software that enables people who are not programmers or software developers to design the foods they want. And then we need a place where people can share digital recipes, like we share music."

From Columbia Engineering   View Full Article

Aeva and Nikon 4D LiDar to Industrial Automation and Metrology

Continuing to look at other LiDAR uses.

Aeva and Nikon to Bring 4D LiDAR to Industrial Automation and Metrology Markets

Leveraging Nikon’s market position in laser radar technology and excellence in industrial precision with Aeva’s industry-first Frequency Modulated Continuous Wave (FMCW) 4D LiDAR on chip, companies to develop next-generation solutions with superior performance at industry-leading costs

Aeva and Nikon positioned to accelerate broad adoption of 4D LiDAR in the $10 billion industrial automation and metrology markets

MOUNTAIN VIEW, Calif.–(BUSINESS WIRE)–Aeva (NYSE: AEVA), a leader in next-generation sensing and perception systems, today announced a strategic collaboration with Nikon Corporation, a global manufacturer and supplier of metrology and inspection equipment for the industrial automation and metrology markets. The companies will focus on bringing Frequency Modulated Continuous Wave (FMCW) 4D LiDAR with unique micron-level measurement capability to high precision industrial automation and metrology applications.  ... '

Robots Doing the Kitchen Labor

 Intriguing to see where this is going, a close observer of process details and their improvement.  Opportunity is to do this right now. 

Amid the Labor Shortage, Robots Step in to Make the French Fries

The Wall Street Journal, Christopher Mims, August 7, 2021

Restauranteurs are implementing robots to automate food production amid a labor shortage and soaring demand for takeout and delivery. Entrepreneur Doug Foreman said increasingly affordable sensors and actuators and more accessible software, combined with automated food-handling systems, adds momentum to this trend. Barney Wragg at London-based robotics company Karakuri said, "The real challenge is, how do you make machines that can manipulate this peculiar, non-conformative, multi-dimensionally deformative substance." Startups are addressing this problem either by automating just one food-preparation step, or preparing one simple meal type, like a bowl. Many restaurateurs and executives considering kitchen automation are attracted to the technology's potential for consistency, reliability, and cost savings.  ... ' 

Automated Vertical Farming

General description without details, but interesting application.   Was involved with some farming applications and it seems some of these can be readily automated with such systems.  Note posts regarding 'agriculture' here.

Robots Take Vertical Farming to New Heights    in ACM By Governing, July 1, 2021

Fifth Season, a vertical farm in Braddock, PA, uses robots to grow greens indoors, from seed to harvest.

Co-founder Austin Webb said, "What we have built is the industry first, and industry only, end-to-end automated platform."Plastic trays with unique IDs are stacked throughout the 60,000-square-foot facility, and every plant can be traced from any point in the growing process.

Proprietary software directs the robots to stack and remove trays of plants.   LEDs are used to replicate sunlight, and sensors are used to monitor the nutrient mix, carbon dioxide levels, and light spectrum.

Compared to conventional farming, Fifth Season uses up to 95% less water, 98% less land, and no herbicides or pesticides.

Moreover, a half-acre indoors is equivalent to almost 100 acres of farmland in terms of production.

From Governing  ... 

Towards Automation

 More on business automation. 

How Businesses Are Perfecting With Artificial Intelligence  by 7wData

Have you heard automation is the new black? And what better automation of a process than using a computer software to perform humanlike activities aka Artificial Intelligence (AI). Whether we realize it or not, Artificial Intelligence is all around us, playing an active role in our daily lives. The fun part is that many of us fear it just because we don’t know its real power and what wonders we can do with it.

Sure, there are pros and cons of AI, but if we just focus on the pros, we can keep the cons under control. Artificial intelligence can not only make our lives better, it can supercharge our businesses. Automotive, healthcare, finance, travel, and so many other industries can perfect their businesses with it. Here are the three key areas of a business that can be automated with AI.

Artificial intelligence is the king of automating the marketing side of your business. I am gonna say that louder for the businesses that run paid ads as a part of their marketing strategy.

AI sorts your customer contacts however you want, interest-wise, demographics-wise, subscription status wise, etc. You can automate and customize emails for different categories of your customers. There are a number of software solutions for automated email marketing. If your business isn’t using one, you are already behind the game.  .... '

Automating Excel with Python

 Nice idea.  In fact can think of several application examples where we were appending to capabilities of existing spreadsheets.   One a much used, almost historical example.  Could have led to quicker prototypes at least.  Pre-familiarity to the data in the spreadsheets.  Our coders probably would not want to deal with excel, but it still has useful capabilities:

Automate Your Excel Using Python

From manual to an automated approach  By Pranjal Saxena

 Python is an amazing programming language. It is easier to learn and adapt. The error messages in python are self-explainable. We don’t need to invest hours to find the solution to our error message. That’s why I love this programming language.

I think this should be the ideal programming language. A programming language's goal should be to help us build exciting products, not wasting time in solving errors.

Recently, I have learned about automating excel tasks using python. I thought of sharing those amazing tricks with a wider audience. This automating strategy use python programming. The good thing is — each line of python code is self explainable that you don’t need to google anything.

You can use the code editor of your choice. In this article, I will be using the Jupyter Notebook for demonstration purpose.   ..... (details below at the link) 

Farm Automation Advances

More picks on my agriculture and botany advances.     Some good related statistics.

Automating the Farm, By Esther Shein, Commissioned by CACM Staff 

 Step inside the cab of the John Deere 8RX Tractor, and you may be surprised at how it resembles an airplane cockpit, with systems and maps that produce lots of data (and a comfortable seat, to boot). Attached to that cab is a planter equipped with 300 sensors and 140 controllers that methodically and precisely deliver seed to the soil in near-perfect rows.

Farming is often an inexact science. As global demands on agriculture continue to grow, Deere and others are looking to change that with precision agriculture technology.

The human population of the world is expected to climb from an estimated 7.8 billion in 2020 to 9.7 billion by 2050 and 11.2 billion by 2100, according to the United Nations World Population Prospects reports. As the population grows, demands on farmers also are growing; they are tasked with feeding the world, but with increasingly less land and fewer resources, while facing the impacts of climate change.

Companies like John Deere and startup EarthSense are looking to address those challenges. Increasingly, robotics, GPS controls, computer vision, and sensors embedded with machine learning are being added to farm equipment to do things like plant seeds, identify weeds so herbicide can be sprayed in the right places, and even pick strawberries.

This is expected to advance the deployment of smart and autonomous farm machinery, according to market research firm McKinsey. "Increasing the autonomy of machinery through better connectivity could create $50 billion to $60 billion of additional value by 2030," the firm said.

In 2018, some 10% to 15% of U.S. farmers were using Internet of Things (IoT) systems across 3.1 billion acres and 250,000 farms, and were collectively spending about $960 million.

Almost 230 million acres were covered by John Deere technology alone in 2020 across more   .... '