The rise of artificial intelligence (AI) holds the promise to fully digitize all manner of verticals, all manner of workflows

and in the age of eCommerce, advanced technology in the service of efficiency can help transform logistics.

Ben Gruettner, vice president of revenue at Robust.AI, told PYMNTS that robots and AI, in tandem with, well, the human touch, can keep up with changing end-market demands and the changing workflows that demand flexibility.

Generative AI is arriving on the warehouse floor faster than many anticipated, he told PYMNTS, adding that Theres no either/or when it comes to workers and robots.

Used collaboratively, much in the way that warehouse workers today make use of forklifts, pallet jacks and hand trucks, robots can help with filling the job gap and helping warehouses and fulfillment centers deal with increasingly high demand.

As reported earlier this year, DHL Supply Chain partnered with Robust.AI to develop and deploy a fleet of warehouse robots specifically, Carter, a collaborative mobile robot that the companies said provide flexible warehouse material handling automation.

Through the joint efforts, the robots handle a range of functions, starting with optimizing the picking process. AI helps the robots learn and adapt to real-time warehouse conditions, optimizing workflows and maximizing productivity. The robots embedded sensors and AI capabilities generate data to improve warehouse layouts, staffing, and inventory management.

Gruettner said that solutions like Carter help support the labor companies intend to keep, adding, We are providing a solution that can be easily deployed into existing operations, aiming to empower and enhance the productivity of warehouse workers within DHL.

AI, he added, helps ensure that the robots adapt as third-party logistics operations change because warehouse volume and customer profiles change throughout the year.

The robots he said, are autonomous allies with zero-friction adoption, he told PYMNTS.

The remarks from Gruettner come in the wake of separate coverage by PYMNTS,where as noted here,Akash Gupta, CEO of GreyOrange, said that only about 10% to 15% of warehouses have mechanized at least some of their processes. And a relatively scant mid-single-digit percentage point slice of warehouses might be deemed to be heavily automated.

He told PYMNTS that the former, traditional ways of managing inventory in batches are no longer enough to satisfy the demands of omnichannel commerce. AI, he said, can offer up an intelligent software orchestration layer that help manage the flow of inventory and data.

In a separate interview with PYMNTS, Yoav Amiel, chief information officer at freight brokerage platform and third-party logistics company RXO, told PYMNTS, If you look at the maturity of AI models over the years, if you go back 20 years, AI was more around recognition, and gradually that evolved into coming up with insights and serving as a recommendation engine.

He added, Today, AI is capable of task completion. If you think about warehouse inventory planning, workforce planning, all of these activities, AI can make these processes much more efficient overall.

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Mobile Robots and AI Help Modernize, Streamline Warehouse Workflows - PYMNTS.com

Clinics around the country are starting to offer patients a new service: having their mammograms read not just by a radiologist, but also by an artificial intelligence model. The hospitals and companies that provide these tools tout their ability to speed the work of radiologists and detect cancer earlier than standard mammograms alone.

Currently, mammograms identify around 87 percent of breast cancers. Theyre more likely to miss cancer in younger women and those with dense breasts. They sometimes lead to false positives that require more testing to rule out cancer, and can also turn up precancerous conditions that may never cause serious problems but nonetheless lead to treatment because its not possible to predict the risk of not treating them.

Its not a perfect science by any stretch, said Dr. John Lewin, chief of breast imaging at Smilow Cancer Hospital and Yale Cancer Center.

Experts are excited by the prospect of improving the accuracy of screening for breast cancer, which 300,000 women are diagnosed with each year in the United States. But they also have concerns about whether these A.I. tools will work well across a diverse range of patients and whether they can meaningfully improve breast cancer survival.

Mammograms contain a wealth of information on breast tissues and ducts. Certain patterns, such as bright white spots with jagged edges, may be a sign of cancer. Fine white lines, by comparison, may indicate calcifications that can be benign or may need more testing. Other patterns can be tricky for humans to differentiate from normal breast tissue.

A.I. models can, in some cases, see what we cannot see, said Dr. Katerina Dodelzon, a radiologist who specializes in breast imaging at NewYork-Presbyterian/Weill Cornell Medical Center.

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Are AI Mammograms Worth the Cost? - The New York Times

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Students sitting for their STAAR exams this week will be part of a new method of evaluating Texas schools: Their written answers on the states standardized tests will be graded automatically by computers.

The Texas Education Agency is rolling out an automated scoring engine for open-ended questions on the State of Texas Assessment of Academic Readiness for reading, writing, science and social studies. The technology, which uses natural language processing, a building block of artificial intelligence chatbots such as GPT-4, will save the state agency about $15-20 million per year that it would otherwise have spent on hiring human scorers through a third-party contractor.

The change comes after the STAAR test, which measures students understanding of state-mandated core curriculum, was redesigned in 2023. The test now includes fewer multiple choice questions and more open-ended questions known as constructed response items. After the redesign, there are six to seven times more constructed response items.

We wanted to keep as many constructed open ended responses as we can, but they take an incredible amount of time to score, said Jose Rios, director of student assessment at the Texas Education Agency.

In 2023, Rios said TEA hired about 6,000 temporary scorers, but this year, it will need under 2,000.

To develop the scoring system, the TEA gathered 3,000 responses that went through two rounds of human scoring. From this field sample, the automated scoring engine learns the characteristics of responses, and it is programmed to assign the same scores a human would have given.

This spring, as students complete their tests, the computer will first grade all the constructed responses. Then, a quarter of the responses will be rescored by humans.

When the computer has low confidence in the score it assigned, those responses will be automatically reassigned to a human. The same thing will happen when the computer encounters a type of response that its programming does not recognize, such as one using lots of slang or words in a language other than English.

We have always had very robust quality control processes with humans, said Chris Rozunick, division director for assessment development at the Texas Education Agency. With a computer system, the quality control looks similar.

Every day, Rozunick and other testing administrators will review a summary of results to check that they match what is expected. In addition to low confidence scores and responses that do not fit in the computers programming, a random sample of responses will also be automatically handed off to humans to check the computers work.

TEA officials have been resistant to the suggestion that the scoring engine is artificial intelligence. It may use similar technology to chatbots such as GPT-4 or Googles Gemini, but the agency has stressed that the process will have systematic oversight from humans. It wont learn from one response to the next, but always defer to its original programming set up by the state.

We are way far away from anything thats autonomous or can think on its own, Rozunick said.

But the plan has still generated worry among educators and parents in a world still weary of the influence of machine learning, automation and AI.

Some educators across the state said they were caught by surprise at TEAs decision to use automated technology also known as hybrid scoring to score responses.

There ought to be some consensus about, hey, this is a good thing, or not a good thing, a fair thing or not a fair thing, said Kevin Brown, the executive director for the Texas Association of School Administrators and a former superintendent at Alamo Heights ISD.

Representatives from TEA first mentioned interest in automated scoring in testimony to the Texas House Public Education Committee in August 2022. In the fall of 2023, the agency announced the move to hybrid scoring at a conference and during test coordinator training before releasing details of the process in December.

The STAAR test results are a key part of the accountability system TEA uses to grade school districts and individual campuses on an A-F scale. Students take the test every year from third grade through high school. When campuses within a district are underperforming on the test, state law allows the Texas education commissioner to intervene.

The commissioner can appoint a conservator to oversee campuses and school districts. State law also allows the commissioner to suspend and replace elected school boards with an appointed board of managers. If a campus receives failing grades for five years in a row, the commissioner is required to appoint a board of managers or close that school.

With the stakes so high for campuses and districts, there is a sense of uneasiness about a computers ability to score responses as well as a human can.

There's always this sort of feeling that everything happens to students and to schools and to teachers and not for them or with them, said Carrie Griffith, policy specialist for the Texas State Teachers Association.

A former teacher in the Austin Independent School District, Griffith added that even if the automated scoring engine works as intended, it's not something parents or teachers are going to trust.

Superintendents are also uncertain.

The automation is only as good as what is programmed, said Lori Rapp, superintendent at Lewisville ISD. School districts have not been given a detailed enough look at how the programming works, Rapp said.

The hybrid scoring system was already used on a limited basis in December 2023. Most students who take the STAAR test in December are retaking it after a low score. Thats not the case for Lewisville ISD, where high school students on an altered schedule test for the first time in December, and Rapp said her district saw a drastic increase in zeroes on constructed responses.

At this time, we are unable to determine if there is something wrong with the test question or if it is the new automated scoring system, Rapp said.

The state overall saw an increase in zeroes on constructed responses in December 2023, but the TEA said there are other factors at play. In December 2022, the only way to score a zero was by not providing an answer at all. With the STAAR redesign in 2023, students can receive a zero for responses that may answer the question but lack any coherent structure or evidence.

The TEA also said that students who are retesting will perform at a different level than students taking the test for the first time. Population difference is driving the difference in scores rather than the introduction of hybrid scoring, a TEA spokesperson said in an email.

For $50, students and their parents can request a rescore if they think the computer or the human got it wrong. The fee is waived if the new score is higher than the initial score. For grades 3-8, there are no consequences on a students grades or academic progress if they receive a low score. For high school students, receiving a minimum STAAR test score is a common way to fulfill one of the state graduation requirements, but it is not the only way.

Even with layers of quality control, Round Rock ISD Superintendent Hafedh Azaiez said he worries a computer could miss certain things that a human being may not be able to miss, and that room for error will impact students who Azaiez said are trying to do his or her best.

Test results will impact how they see themselves as a student, Brown said, and it can be humiliating for students who receive low scores. With human graders, Brown said, students were rewarded for having their own voice and originality in their writing, and he is concerned that computers may not be as good at rewarding originality.

Julie Salinas, director of assessment, research and evaluation at Brownsville ISD said she has concerns about whether hybrid scoring is allowing the students the flexibility to respond in a way that they can demonstrate their full capability and thought process through expressive writing.

Brownsville ISD is overwhelmingly Hispanic. Students taking an assessment entirely in Spanish will have their tests graded by a human. If the automated scoring engine works as intended, responses that include some Spanish words or colloquial, informal terms will be flagged by the computer and assigned to a human so that more creative writing can be assessed fairly.

The system is designed so that it does not penalize students who answer differently, who are really giving unique answers, Rozuick said.

With the computer scoring now a part of STAAR, Salinas is focused on adapting. The district is incorporating tools with automated scoring into how teachers prepare students for the STAAR test to make sure they are comfortable.

Our district is on board and on top of the things that we need to do to ensure that our students are successful, she said.

Disclosure: Google, the Texas Association of School Administrators and Texas State Teachers Association have been financial supporters of The Texas Tribune, a nonprofit, nonpartisan news organization that is funded in part by donations from members, foundations and corporate sponsors. Financial supporters play no role in the Tribune's journalism. Find a complete list of them here.

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Texas will use computers to grade written answers on this year's STAAR tests - The Texas Tribune

Since ChatGPT proved a consumer hit, a gold rush has set off for AI in Silicon Valley. Investors are intrigued by companies promising generative AI will transform the world, and companies seek workers with the skills to bring them into the future. The frenzy may be cooling down in 2024, but AI skills are still hot in the tech market.

Looking to join the AI industry? Which route into the profession is best for each individual learner will depend on that persons current skill level and their target skill or job title.

When assessing online courses, we examined the reliability and popularity of the provider, the depth and variety of topics offered, the practicality of the information, the cost and the duration. The courses and certification programs vary a lot, so choose the options that are right for each person or business.

They are listed in order of skill level and, within the skill level categories, alphabetically. In most cases, each provider offers multiple courses in different aspects of generative AI. Explore these generative AI courses to see which might fit the right niche.

A name learners are likely to see on AI courses a lot is Andrew Ng; he is an adjunct professor at Stanford University, founder of DeepLearning.AI and cofounder of Coursera. Ng is one of the authors of a 2009 paper on using GPUs for deep learning, which NVIDIA and other companies are now doing to transform AI hardware. Ng is the instructor and driving force behind AI for Everyone, a popular, self-paced course more than one million people have enrolled. AI for Everyone from Coursera contains four modules:

For individuals, a Coursera account is $49-$79 per month with a 7-day free trial, depending on the course and plan. However, the AI for Everyone course can be taken for free; the $79 per month fee provides access to graded assignments and earning a certificate.

Coursera states the class takes six hours to complete.

This course has no prerequisites.

Are you a C-suite leader looking to shape your companys vision for generative AI? If so, this non-technical course helps business leaders build a top-down philosophy around generative AI projects. It could be useful for sparking conversation between business and technical leaders.

Free if completed within the Coursera 7-day trial. Otherwise, a Coursera account is $49-$79 per month, depending on the course and plan.

This course takes about one hour.

There are no prerequisites for this course.

This is a well-reviewed beginner course that sets itself apart by approaching AI holistically, including its practical applications and potential social impact. It includes hands-on exercises but doesnt require the learner to know how to code, making it a good mix of practical and beginner content. Datacamps Understanding Artificial Intelligence course is particularly interesting because it includes a section on business and enterprise. Business leaders looking for a non-technical explanation of infrastructure and skills they need to harness AI might be interested in this course.

This course can be accessed with a DataCamp subscription, which costs $25 per person per month, billed annually. Educators can get a group subscription for free.

Including videos and exercises, this course lasts about two hours.

This course has no prerequisites.

Google Clouds Introduction to Generative AI Learning Path covers what generative AI and large language models are for beginners. Since its from Google, it provides some specific Google applications used to build generative AI: Google Tools and Vertex AI. It includes a section on responsible AI, inviting the learner to consider ethical practices around the generative AI they may go on to create. Completing this learning path will award the Prompt Design in Vertex AI skill badge.

Another option from Google Cloud is the Generative AI for Developers Learning Path.

This course is free.

The path technically contains 8 hours and 30 minutes of content, but some of that content is quizzes. The time it takes for each individual to complete the path may vary.

The path has no prerequisites.

Since this course is taught by an IBM professional, it is likely to include contemporary, real-world insight into how generative AI and machine learning are used today. It is an eight-hour course that covers a wide range of topics around artificial intelligence, including ethical concerns. Introduction to Artificial Intelligence includes quizzes and can contribute to career certificates in a variety of programs from Coursera.

Free if completed within the 7-day Coursera free trial, or $49-$79 per month afterward, depending on the course and plan. Financial aid is available.

Coursera estimates this course will take about eight hours.

There are no prerequisites for this course.

AWS offers a lot of AI-related courses and programs, but we chose this one because it combines fundamentals the first two courses in the developer kit with hands-on knowledge and training on specific AWS products. This could be very practical for someone whose organization already works with multiple AWS products but wants to expand into more generative AI products and services. This online, self-guided kit includes hands-on labs and AWS Jam challenges, which are gamified and AI-powered experiences.

The AWS Generative AI Developer Kit is part of the AWS Skill Builder subscription. AWS Skill Builder is accessible with a 7-day trial, after which it costs $29 per month or $449 per year.

The courses take 16 hours and 30 minutes to complete.

This course is appropriate for professionals who have not worked with generative AI before, but it would help to have worked within the AWS ecosystem. In particular, Amazon Bedrock is discussed at such a level that it would be beneficial to have completed the course AWS Technical Essentials or have comparable real-world experience.

Harvards online professional certificate combines the venerable universitys Introduction to Computer Science course with another course tailored to careers in AI: Introduction to Artificial Intelligence with Python. This certification is suitable for people who want to become software developers with a focus on AI. This course is self-paced, and students will receive pre-recorded instruction from Harvard University faculty.

Both courses together cost $466.20 as of the time of writing; this is a discounted price from the usual $518. Learners can take both courses in the certification for free, but the certification itself requires a fee.

These courses are self paced, but the estimated time for completion is five months at 7-22 hours per week.

There are no prerequisites required, although a high-school level of experience with programming basics would likely provide a solid foundation. The Introduction to Computer Science course covers algorithms and programming in C, Python, SQL and JavaScript, as well as CSS and HTML.

MIT has played a leading role in the rise of AI and the new category of jobs it is creating across the world economy, the description of the program states, summing up the educational legacy behind this course. MITs AI and machine learning certification course for professionals is taught by MIT faculty who are working at the cutting edge of the field.

This certification program is comparable to a traditional college course, and that level of commitment is reflected in the price.

If a learner completes at least 16 days of qualifying courses, they will be eligible to receive the certificate. Courses are typically taught June, July and August online or on MITs campus.

There is an application fee of $325. The two mandatory courses are:

The remaining required 11 days can be composed of elective classes, which last between two and five days each and cost between $2,500 and $4,700 each.

16 days.

The Professional Certificate Program in Machine Learning & Artificial Intelligence is designed for technical professionals with at least three years of experience in computer science, statistics, physics or electrical engineering. In particular, MIT recommends this program for anyone whose work intersects with data analysis or for managers who need to learn more about predictive modeling.

Completion of the academically rigorous Stanford Artificial Intelligence Professional Program will result in a certification. This program is suitable for professionals who want to learn how to build AI models from scratch and then fine-tune them for their businesses. In addition, it helps professionals understand research results and conduct their own research on AI. This program offers 1 to 1 time with professionals in the industry and some flexibility learners can take all eight courses in the program or choose individual courses.

The individual courses are:

The Stanford Artificial Intelligence Professional Program costs $1,750 per course. Learners who complete three courses will earn a certificate.

Each course lasts 10 weeks at 10 to 15 hours per week. Courses are held on set dates.

Interested professionals can submit an application; applicants are asked to prove competence in the following areas:

Udacitys Artificial Intelligence Nanodegree program equips graduates with practical knowledge about how to solve mathematical problems using artificial intelligence. This class isnt about generative AI models; instead, it teaches the underpinnings of traditional search algorithms, probabilistic graphical models, and planning and scheduling systems. Learners who complete this course will gain experience in working with the types of algorithms used in the real world for:

This course costs $249 per month paid monthly or $846 for the first four months of the subscription, after which it will cost $249 per month.

This course lasts about three months.

Learners in this course should have a background in programming and mathematics. The following skills are recommended:

Whether it is worth taking an AI course depends on many factors: the course, the individual and the job market. For instance, getting an AI-focused certification might contribute to getting a salary increase or making a career change. AI courses could help someone learn AI skills that might be a good fit for their abilities, or could be the first step toward a lucrative and life-long career. Educating oneself in a contemporary topic can always have some benefits in terms of practicing new skills.

Some introductory AI courses do not require coding; however, AI is a relatively complex topic in computing, and practitioners will need some programming skills as they progress to more advanced courses and learn how to build and deploy AI models. Most likely, intermediate learners need to be comfortable working in Python.

SEE: Help your business by becoming your own IT expert. (TechRepublic Academy)

Some of these courses and certifications include education in basic programming and computer science. More advanced courses and certifications will require learners to already have a college-level knowledge of calculus, linear algebra, probability and statistics, as well as coding.

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The 10 Best AI Courses That Are Worth Taking in 2024 - TechRepublic