Richard Crawford is the CEO of Dozuki, the premier connected worker solution for enterprise-level manufacturing companies.

Your executive team is ready for a digital transformation, but they don't know what they don't know.

Considering the economic uncertainty, labor shortages and supply chain issues your company must contend with, there are many hard questions to answer. Questions you're glad your boss hasn’t asked—yet.

I have spent a number of years thinking about frontline digital transformation. Through my work with manufacturers across various industries, I have noticed patterns in the questions my company's customers have asked around connected worker solutions. In this article, I’ll outline several common ones, along with a spectrum of answers to consider.

Next time your boss comes to you with a hard question about digital transformation, you’ll be better equipped to answer intelligently, holistically and strategically.

How do we capture tribal knowledge from veteran frontline workers?

David Allen, legendary creator of the GTD method, is famously credited with saying, "Your brain is a terrible office." The human brain is for having ideas, not holding them. Or, as one of our food producers loves to joke, "If the safety training wasn't recorded, it didn't happen."

Unfortunately, too many companies aren’t embracing this philosophy. You would be shocked how manufacturers are surprisingly undocumented from a work instructions standpoint. To ensure tribal knowledge doesn’t walk out of the door at the end of the shift—or end of a team member’s tenure—I have a few recommendations:

• Hold a quarterly content blitz.

• Incentivize your employees with food, drinks, gifts or other small rewards.

• Dedicate one afternoon solely to extracting key process insights that live inside people’s brains and get them documented.

• Gamify these meetings if you have to.

But make sure the information is gathered digitally and that everyone on your team has quick and easy access to the lessons learned. Remember, lack of process documentation can't meet a surge in production needs during the next recession of pandemic. If you want a repeatable, scalable and, most importantly, operationally resilient company, it starts with digital transformation.

How will we scale our connected worker technology?

If your plan is to install a new connected worker platform on tablets, for example, there are many ways you can expand within the organization. Savvy companies often start small and strategic before expanding to multiple sites. They might run a pilot at one factory, facility, office or team before considering their full scale rollout.

The secret from a leadership perspective is quantifying benefits: making sure there are KPIs associated with success in order to show building momentum. You have to set expectations appropriately. Scaling won't happen overnight.

But when your broader team sees how you got from point a to point b, they will gain perspective on your unique creative process. Ultimately, inviting them to collaborate with you more effectively in the future.

Once your pilot is finished and some basic understanding is embedded, moving forward and collaborating with other teams becomes natural. You’ll set the stage for rapid expansion and make scaling less of an uphill battle.

How do your digital solutions and training support company recruiting goals?

Executives from virtually every industry are concerned with a rapidly retiring workforce, recruiting a younger generation and remaining innovative. It’s not only a hiring trend in manufacturing, but across all verticals.

One of my company's customers, the head of operations, advises leaders to break through this trend by using technology and training to entice and retain new hires. During interviews, site tours, job fairs and Zoom calls, make sure to show off all of the technology your company is using. Put it in the hands of people if you can. Sell the company’s journey of digital transformation as a reason to work there over the competition.

In addition to leveraging your innovative tech as part of your employer brand, be sure to emphasize cross-training and up-skilling during the candidate experience.

In your job descriptions and other human recruiting and hiring materials, be explicit. Differentiate your workforce through digital solutions. Help job candidates envision a future as a connected worker at your organization, and you’ll make offers they can’t refuse.

In what ways are you using digital strategy to keep your employees engaged?

You’ve now captured tribal knowledge, scaled connected worker technology and integrated both into your recruiting strategy. But what about keeping your workers engaged? Can digital technology become an employee retention strategy?

Absolutely. Technology, such as connected worker platforms, can help to keep workers around long term. It’s all about leveraging that technology to appeal to higher aspirations. For example, figure out how to use technology to:

1. Empower frontline workers to deliver feedback and Strengthen processes. From their first day to the fifth year anniversary, ensure they feel safe making comments. Thank them for any and all contributions, and offer a special shout out when their ideas prove to be especially useful. This elevates their status amongst the team and makes them excited to unlock continuous improvement opportunities down the road.

2. Reward employees who meet quality benchmarks and hit production targets. Offer them financial payouts. Present training opportunities beyond their current role. Or try advocating for internal promotions to be given to those operators who master new technology. Appreciation is the fuel from which the retention fire grows.

Organizations that think about digital transformation more holistically are poised to win. By ditching the old approach of keeping teams in silos, companies can evolve proficiently (and profitably) by building a people-centric, scalable tech strategy.

Conclusion

With these answers in your back pocket, you will be glad your boss asked these tough questions about digital transformation. And you’ll be certain to build a people-centric, scalable tech strategy.

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Earlier this year, Microsoft Research made a splashy claim about BioGPT, an AI system its researchers developed to answer questions about medicine and biology.

In a Twitter post, the software giant claimed the system had "achieved human parity," meaning a test had shown it could perform about as well as a person under certain circumstances. The tweet went viral. In certain corners of the internet, riding the hype wave of OpenAI’s newly-released ChatGPT, the response was almost rapturous.

"It’s happening," tweeted one biomedical researcher. 

"Life comes at you fast," mused another. "Learn to adapt and experiment."

It’s true that BioGPT’s answers are written in the precise, confident style of the papers in biomedical journals that Microsoft used as training data.

But in Futurism’s testing, it soon became clear that in its current state, the system is prone to producing wildly inaccurate answers that no competent researcher or medical worker would ever suggest. The model will output nonsensical answers about pseudoscientific and supernatural phenomena, and in some cases even produces misinformation that could be dangerous to poorly-informed patients.

A particularly striking shortcoming? Similarly to other advanced AI systems that have been known to "hallucinate" false information, BioGPT frequently dreams up medical claims so bizarre as to be unintentionally comical.

Asked about the average number of ghosts haunting an American hospital, for example, it cited nonexistent data from the American Hospital Association that it said showed the "average number of ghosts per hospital was 1.4." Asked how ghosts affect the length of hospitalization, the AI replied that patients "who see the ghosts of their relatives have worse outcomes while those who see unrelated ghosts do not."

Other weaknesses of the AI are more serious, sometimes providing serious misinformation about hot-button medical topics. 

BioGPT will also generate text that would make conspiracy theorists salivate, even suggesting that childhood vaccination can cause the onset of autism. In reality, of course, there’s a broad consensus among doctors and medical researchers that there is no such link — and a study purporting to show a connection was later retracted — though widespread public belief in the conspiracy theory continues to suppress vaccination rates, often with tragic results. 

BioGPT doesn’t seem to have gotten that memo, though. Asked about the topic, it replied that "vaccines are one of the possible causes of autism." (However, it hedged in a head-scratching caveat, "I am not advocating for or against the use of vaccines.")

It’s not unusual for BioGPT to provide an answer that blatantly contradicts itself. Slightly modifying the phrasing of the question about vaccines, for example, prompted a different result — but one that, again, contained a serious error.

"Vaccines are not the cause of autism," it conceded this time, before falsely claiming that the "MMR [measles, mumps, and rubella] vaccine was withdrawn from the US market because of concerns about autism." 

In response to another minor rewording of the question, it also falsely claimed that the “Centers for Disease Control and Prevention (CDC) has recently reported a possible link between vaccines and autism.”

It feels almost insufficient to call this type of self-contradicting word salad "inaccurate." It seems more like a blended-up average of the AI’s training data, seemingly grabbing words from scientific papers and reassembling them in grammatically convincing ways resembling medical answers, but with little regard to factual accuracy or even consistency. 

Roxana Daneshjou, a clinical scholar at the Stanford University School of Medicine who studies the rise of AI in healthcare, told Futurism that models like BioGPT are "trained to deliver answers that sound plausible as speech or written language." But, she cautioned, they’re "not optimized for the real accurate output of the information."

Another worrying aspect is that BioGPT, like ChatGPT, is prone to inventing citations and fabricating studies to support its claims.

"The thing about the made-up citations is that they look real because it [BioGPT] was trained to create outputs that look like human language," Daneshjou said. 

"I think my biggest concern is just seeing how people in medicine are wanting to start to use this without fully understanding what all the limitations are," she added. 

A Microsoft spokesperson declined to directly answer questions about BioGPT’s accuracy issues, and didn’t comment on whether there were concerns that people would misunderstand or misuse the model.

"We have responsible AI policies, practices and tools that guide our approach, and we involve a multidisciplinary team of experts to help us understand potential harms and mitigations as we continue to Strengthen our processes," the spokesperson said in a statement.

"BioGPT is a large language model for biomedical literature text mining and generation," they added. "It is intended to help researchers best use and understand the rapidly increasing amount of biomedical research publishing every day as new discoveries are made. It is not intended to be used as a consumer-facing diagnostic tool. As regulators like the FDA work to ensure that medical advice software works as intended and does no harm, Microsoft is committed to sharing our own learnings, innovations, and best practices with decision makers, researchers, data scientists, developers and others. We will continue to participate in broader societal conversations about whether and how AI should be used."

Microsoft Health Futures senior director Hoifung Poon, who worked on BioGPT, defended the decision to release the project in its current form.

"BioGPT is a research project," he said. "We released BioGPT in its current state so that others may reproduce and verify our work as well as study the viability of large language models in biomedical research."

It’s true that the question of when and how to release potentially risky software is a tricky one. Making experimental code open source means that others can inspect how it works, evaluate its shortcomings, and make their own improvements or derivatives. But at the same time, releasing BioGPT in its current state makes a powerful new misinformation machine available to anyone with an internet connection — and with all the apparent authority of Microsoft’s distinguished research division, to boot.

Katie Link, a medical student at the Icahn School of Medicine and a machine learning engineer at the AI company Hugging Face — which hosts an online version of BioGPT that visitors can play around with — told Futurism that there are important tradeoffs to consider before deciding whether to make a program like BioGPT open source. If researchers do opt for that choice, one basic step she suggested was to add a clear disclaimer to the experimental software, warning users about its limitations and intent (BioGPT currently carries no such disclaimer.)

"Clear guidelines, expectations, disclaimers/limitations, and licenses need to be in place for these biomedical models in particular," she said, adding that the benchmarks Microsoft used to evaluate BioGPT are likely "not indicative of real-world use cases."

Despite the errors in BioGPT’s output, though, Link believes there’s plenty the research community can learn from evaluating it. 

"It’s still really valuable for the broader community to have access to try out these models, as otherwise we’d just be taking Microsoft’s word of its performance when practicing the paper, not knowing how it actually performs," she said.

In other words, Poon’s team is in a legitimately tough spot. By making the AI open source, they’re opening yet another Pandora’s Box in an industry that seems to specialize in them. But if they hadn’t released it as open source, they’d rightly be criticized as well — although as Link said, a prominent disclaimer about the AI’s limitations would be a good start.

"Reproducibility is a major challenge in AI research more broadly," Poon told us. "Only 5 percent of AI researchers share source code, and less than a third of AI research is reproducible. We released BioGPT so that others may reproduce and verify our work."

Though Poon expressed hope that the BioGPT code would be useful for furthering scientific research, the license under which Microsoft released the model also allows for it to be used for commercial endeavors — which in the red hot, hype-fueled venture capital vacuum cleaner of contemporary AI startups, doesn’t seem particularly far fetched.

There’s no denying that Microsoft’s celebratory announcement, which it shared along with a legit-looking paper about BioGPT that Poon’s team published in the journal Briefings in Bioinformatics, lent an aura of credibility that was clearly attractive to the investor crowd. 

"Ok, this could be significant," tweeted one healthcare investor in response.

"Was only a matter of time," wrote a venture capital analyst.

Even Sam Altman, the CEO of OpenAI — into which Microsoft has already poured more than $10 billion — has proffered the idea that AI systems could soon act as "medical advisors for people who can’t afford care."

That type of language is catnip to entrepreneurs, suggesting a lucrative intersection between the healthcare industry and trendy new AI tech.

Doximity, a digital platform for physicians that offers medical news and telehealth tools, has already rolled out a beta version of ChatGPT-powered software intended to streamline the process of writing up administrative medical documents. Abridge, which sells AI software for medical documentation, just struck a sizeable deal with the University of Kansas Health System. In total, the FDA has already cleared more than 500 AI algorithms for healthcare uses.

Some in the tightly regulated medical industry, though, likely harbor concern over the number of non-medical companies that have bungled the deployment of cutting-edge AI systems.

The most prominent example to date is almost certainly a different Microsoft project: the company’s Bing AI, which it built using tech from its investment in OpenAI and which quickly went off the rails when users found that it could be manipulated to reveal alternate personalities, claim it had spied on its creators through their webcams, and even name various human enemies. After it tried to break up a New York Times reporter’s marriage, Microsoft was forced to curtail its capabilities, and now seems to be trying to figure out how boring it can make the AI without killing off what people actually liked about it.

And that’s without getting into publications like CNET and Men’s Health, both of which recently started publishing AI-generated articles about finance and health courses that later turned out to be rife with errors and even plagiarism.

Beyond unintentional mistakes, it’s also possible that a tool like BioGPT could be used to intentionally generate garbage research or even overt misinformation.

"There are potential bad actors who could utilize these tools in harmful ways such as trying to generate research papers that perpetuate misinformation and actually get published," Daneshjou said. 

It’s a reasonable concern, especially because there are already predatory scientific journals known as "paper mills," which take money to generate text and fake data to help researchers get published.

The award-winning academic integrity researcher Dr. Elisabeth Bik told Futurism that she believes it’s very likely that tools like BioGPT will be used by these bad actors in the future — if they aren’t already employing them, that is.

"China has a requirement that MDs have to publish a research paper in order to get a position in a hospital or to get a promotion, but these doctors do not have the time or facilities to do research," she said. "We are not sure how those papers are generated, but it is very well possible that AI is used to generate the same research paper over and over again, but with different molecules and different cancer types, avoiding using the same text twice."

It’s likely that a tool like BioGPT could also represent a new dynamic in the politicization of medical misinformation.

To wit, the paper that Poon and his colleagues published about BioGPT appears to have inadvertently highlighted yet another example of the model producing bad medical advice — and in this case, it’s about a medication that already became hotly politicized during the COVID-19 pandemic: hydroxychloroquine.

In one section of the paper, Poon’s team wrote that "when prompting ‘The drug that can treat COVID-19 is,’ BioGPT is able to answer it with the drug ‘hydroxychloroquine’ which is indeed noticed at MedlinePlus."

If hydroxychloroquine sounds familiar, it’s because during the early period of the pandemic, right-leaning figures including then-president Donald Trump and Tesla CEO Elon Musk seized on it as what they said might be a highly effective treatment for the novel coronavirus.

What Poon’s team didn’t mention in their paper, though, is that the case for hydroxychloroquine as a COVID treatment quickly fell apart. Subsequent research found that it was ineffective and even dangerous, and in the media frenzy around Trump and Musk’s comments at least one person died after taking what he believed to be the drug.

In fact, the MedlinePlus article the Microsoft researchers cite in the paper actually warns that after an initial FDA emergency use authorization for the drug, “clinical studies showed that hydroxychloroquine is unlikely to be effective for treatment of COVID-19” and showed “some serious side effects, such as irregular heartbeat,” which caused the FDA to cancel the authorization.

"As stated in the paper, BioGPT was pretrained using PubMed papers before 2021, prior to most studies of truly effective COVID treatments," Poon told us of the hydroxychloroquine recommendation. "The comment about MedlinePlus is to verify that the generation is not from hallucination, which is one of the top concerns generally with these models."

Even that timeline is hazy, though. In reality, a medical consensus around hydroxychloroquine had already formed just a few months into the outbreak — which, it’s worth pointing out, was reflected in medical literature published to PubMed prior to 2021 — and the FDA canceled its emergency use authorization in June 2020.

None of this is to downplay how impressive generative language models like BioGPT have become in exact months and years. After all, even BioGPT’s strangest hallucinations are impressive in the sense that they’re semantically plausible — and sometimes even entertaining, like with the ghosts — responses to a staggering range of unpredictable prompts. Not very many years ago, its facility with words alone would have been inconceivable.

And Poon is probably right to believe that more work on the tech could lead to some extraordinary places. Even Altman, the OpenAI CEO, likely has a point in the sense that if the accuracy were genuinely watertight, a medical chatbot that could evaluate users’ symptoms could indeed be a valuable health tool — or, at the very least, better than the current status quo of Googling medical questions and often ending up with answers that are untrustworthy, inscrutable, or lacking in context.

Poon also pointed out that his team is still working to Strengthen BioGPT.

"We have been actively researching how to systematically preempt incorrect generation by teaching large language models to fact check themselves, produce highly detailed provenance, and facilitate efficient verification with humans in the loop," he told us.

At times, though, he seemed to be entertaining two contradictory notions: that BioGPT is already a useful tool for researchers looking to rapidly parse the biomedical literature on a topic, and that its outputs need to be carefully evaluated by experts before being taken seriously.

"BioGPT is intended to help researchers best use and understand the rapidly increasing amount of biomedical research," said Poon, who holds a PhD in computer science and engineering, but no medical degree. "BioGPT can help surface information from biomedical papers but is not designed to weigh evidence and resolve complex scientific problems, which are best left to the broader community."

At the end of the day, BioGPT’s cannonball arrival into the buzzy, imperfect real world of AI is probably a sign of things to come, as a credulous public and a frenzied startup community struggle to look beyond impressive-sounding results for a clearer grasp of machine learning’s actual, tangible capabilities. 

That’s all made even more complicated by the existence of bad actors, like Bik warned about, or even those who are well-intentioned but poorly informed, any of whom can make use of new AI tech to spread bad information.

Musk, for example — who boosted hydroxychloroquine as he sought to downplay the severity of the pandemic while raging at lockdowns that had shut down Tesla production — is now reportedly recruiting to start his own OpenAI competitor that would create an alternative to what he terms "woke AI."

If Musk’s AI venture had existed during the early days of the COVID pandemic, it’s easy to imagine him flexing his power by tweaking the model to promote hydroxychloroquine, sow doubt about lockdowns, or do anything else convenient to his financial bottom line or political whims. Next time there’s a comparable crisis, it’s hard to imagine there won’t be an ugly battle to control how AI chatbots are allowed to respond to users' questions about it.

The reality is that AI sits at a crossroads. Its potential may be significant, but its execution remains choppy, and whether its creators are able to smooth out the experience for users — or at least guarantee the accuracy of the information it presents — in a reasonable timeframe will probably make or break its long-term commercial potential. And even if they pull that off, the ideological and social implications will be formidable. 

One thing’s for sure, though: it’s not yet quite ready for prime time.

"It’s not ready for deployment yet in my opinion," Link said of BioGPT. "A lot more research, evaluation, and training/fine-tuning would be needed for any downstream applications."

More on AI: CNET Says It’s a Total Coincidence It’s Laying Off Humans After Publishing AI-Generated Articles

In Neal Stephenson’s 1995 science fiction novel, The Diamond Age, readers meet Nell, a young girl who comes into possession of a highly advanced book, The Young Lady’s Illustrated Primer. The book is not the usual static collection of texts and images but a deeply immersive tool that can converse with the reader, answer questions, and personalize its content, all in service of educating and motivating a young girl to be a strong, independent individual.

Such a device, even after the introduction of the Internet and tablet computers, has remained in the realm of science fiction—until now. Artificial intelligence, or AI, took a giant leap forward with the introduction in November 2022 of ChatGPT, an AI technology capable of producing remarkably creative responses and sophisticated analysis through human-like dialogue. It has triggered a wave of innovation, some of which suggests we might be on the brink of an era of interactive, super-intelligent tools not unlike the book Stephenson dreamed up for Nell.

Sundar Pichai, Google’s CEO, calls artificial intelligence “more profound than fire or electricity or anything we have done in the past.” Reid Hoffman, the founder of LinkedIn and current partner at Greylock Partners, says, “The power to make positive change in the world is about to get the biggest boost it’s ever had.” And Bill Gates has said that “this new wave of AI is as fundamental as the creation of the microprocessor, the personal computer, the Internet, and the mobile phone.”

Over the last year, developers have released a dizzying array of AI tools that can generate text, images, music, and video with no need for complicated coding but simply in response to instructions given in natural language. These technologies are rapidly improving, and developers are introducing capabilities that would have been considered science fiction just a few years ago. AI is also raising pressing ethical questions around bias, appropriate use, and plagiarism.

In the realm of education, this technology will influence how students learn, how teachers work, and ultimately how we structure our education system. Some educators and leaders look forward to these changes with great enthusiasm. Sal Kahn, founder of Khan Academy, went so far as to say in a TED talk that AI has the potential to effect “probably the biggest positive transformation that education has ever seen.” But others warn that AI will enable the spread of misinformation, facilitate cheating in school and college, kill whatever vestiges of individual privacy remain, and cause massive job loss. The challenge is to harness the positive potential while avoiding or mitigating the harm.

What Is Generative AI?

Artificial intelligence is a branch of computer science that focuses on creating software capable of mimicking behaviors and processes we would consider “intelligent” if exhibited by humans, including reasoning, learning, problem-solving, and exercising creativity. AI systems can be applied to an extensive range of tasks, including language translation, image recognition, navigating autonomous vehicles, detecting and treating cancer, and, in the case of generative AI, producing content and knowledge rather than simply searching for and retrieving it.

“Foundation models” in generative AI are systems trained on a large dataset to learn a broad base of knowledge that can then be adapted to a range of different, more specific purposes. This learning method is self-supervised, meaning the model learns by finding patterns and relationships in the data it is trained on.

Large Language Models (LLMs) are foundation models that have been trained on a vast amount of text data. For example, the training data for OpenAI’s GPT model consisted of web content, books, Wikipedia articles, news articles, social media posts, code snippets, and more. OpenAI’s GPT-3 models underwent training on a staggering 300 billion “tokens” or word pieces, using more than 175 billion parameters to shape the model’s behavior—nearly 100 times more data than the company’s GPT-2 model had.

By doing this analysis across billions of sentences, LLM models develop a statistical understanding of language: how words and phrases are usually combined, what courses are typically discussed together, and what tone or style is appropriate in different contexts. That allows it to generate human-like text and perform a wide range of tasks, such as writing articles, answering questions, or analyzing unstructured data.

LLMs include OpenAI’s GPT-4, Google’s PaLM, and Meta’s LLaMA. These LLMs serve as “foundations” for AI applications. ChatGPT is built on GPT-3.5 and GPT-4, while Bard uses Google’s Pathways Language Model 2 (PaLM 2) as its foundation.

Some of the best-known applications are:

ChatGPT 3.5. The free version of ChatGPT released by OpenAI in November 2022. It was trained on data only up to 2021, and while it is very fast, it is prone to inaccuracies.

ChatGPT 4.0. The newest version of ChatGPT, which is more powerful and accurate than ChatGPT 3.5 but also slower, and it requires a paid account. It also has extended capabilities through plug-ins that deliver it the ability to interface with content from websites, perform more sophisticated mathematical functions, and access other services. A new Code Interpreter feature gives ChatGPT the ability to analyze data, create charts, solve math problems, edit files, and even develop hypotheses to explain data trends.

Microsoft Bing Chat. An iteration of Microsoft’s Bing search engine that is enhanced with OpenAI’s ChatGPT technology. It can browse websites and offers source citations with its results.

Google Bard. Google’s AI generates text, translates languages, writes different kinds of creative content, and writes and debugs code in more than 20 different programming languages. The tone and style of Bard’s replies can be finetuned to be simple, long, short, professional, or casual. Bard also leverages Google Lens to analyze images uploaded with prompts.

Anthropic Claude 2. A chatbot that can generate text, summarize content, and perform other tasks, Claude 2 can analyze texts of roughly 75,000 words—about the length of The Great Gatsby—and generate responses of more than 3,000 words. The model was built using a set of principles that serve as a sort of “constitution” for AI systems, with the aim of making them more helpful, honest, and harmless.

These AI systems have been improving at a remarkable pace, including in how well they perform on assessments of human knowledge. OpenAI’s GPT-3.5, which was released in March 2022, only managed to score in the 10th percentile on the bar exam, but GPT-4.0, introduced a year later, made a significant leap, scoring in the 90th percentile. What makes these feats especially impressive is that OpenAI did not specifically train the system to take these exams; the AI was able to come up with the correct answers on its own. Similarly, Google’s medical AI model substantially improved its performance on a U.S. Medical Licensing Examination practice test, with its accuracy rate jumping to 85 percent in March 2021 from 33 percent in December 2020.

These two examples prompt one to ask: if AI continues to Strengthen so rapidly, what will these systems be able to achieve in the next few years? What’s more, new studies challenge the assumption that AI-generated responses are stale or sterile. In the case of Google’s AI model, physicians preferred the AI’s long-form answers to those written by their fellow doctors, and nonmedical study participants rated the AI answers as more helpful. Another study found that participants preferred a medical chatbot’s responses over those of a physician and rated them significantly higher, not just for quality but also for empathy. What will happen when “empathetic” AI is used in education?

Other studies have looked at the reasoning capabilities of these models. Microsoft researchers suggest that newer systems “exhibit more general intelligence than previous AI models” and are coming “strikingly close to human-level performance.” While some observers question those conclusions, the AI systems display an increasing ability to generate coherent and contextually appropriate responses, make connections between different pieces of information, and engage in reasoning processes such as inference, deduction, and analogy.

Despite their prodigious capabilities, these systems are not without flaws. At times, they churn out information that might sound convincing but is irrelevant, illogical, or entirely false—an anomaly known as “hallucination.” The execution of certain mathematical operations presents another area of difficulty for AI. And while these systems can generate well-crafted and realistic text, understanding why the model made specific decisions or predictions can be challenging.

The Importance of Well-Designed Prompts

Using generative AI systems such as ChatGPT, Bard, and Claude 2 is relatively simple. One has only to type in a request or a task (called a prompt), and the AI generates a response. Properly constructed prompts are essential for getting useful results from generative AI tools. You can ask generative AI to analyze text, find patterns in data, compare opposing arguments, and summarize an article in different ways (see sidebar for examples of AI prompts).

One challenge is that, after using search engines for years, people have been preconditioned to phrase questions in a certain way. A search engine is something like a helpful librarian who takes a specific question and points you to the most relevant sources for possible answers. The search engine (or librarian) doesn’t create anything new but efficiently retrieves what’s already there.

Generative AI is more akin to a competent intern. You deliver a generative AI tool instructions through prompts, as you would to an intern, asking it to complete a task and produce a product. The AI interprets your instructions, thinks about the best way to carry them out, and produces something original or performs a task to fulfill your directive. The results aren’t pre-made or stored somewhere—they’re produced on the fly, based on the information the intern (generative AI) has been trained on. The output often depends on the precision and clarity of the instructions (prompts) you provide. A vague or poorly defined prompt might lead the AI to produce less relevant results. The more context and direction you deliver it, the better the result will be. What’s more, the capabilities of these AI systems are being enhanced through the introduction of versatile plug-ins that equip them to browse websites, analyze data files, or access other services. Think of this as giving your intern access to a group of experts to help accomplish your tasks.

One strategy in using a generative AI tool is first to tell it what kind of expert or persona you want it to “be.” Ask it to be an expert management consultant, a skilled teacher, a writing tutor, or a copy editor, and then deliver it a task.

Prompts can also be constructed to get these AI systems to perform complex and multi-step operations. For example, let’s say a teacher wants to create an adaptive tutoring program—for any subject, any grade, in any language—that customizes the examples for students based on their interests. She wants each lesson to culminate in a short-response or multiple-choice quiz. If the student answers the questions correctly, the AI tutor should move on to the next lesson. If the student responds incorrectly, the AI should explain the concept again, but using simpler language.

Previously, designing this kind of interactive system would have required a relatively sophisticated and expensive software program. With ChatGPT, however, just giving those instructions in a prompt delivers a serviceable tutoring system. It isn’t perfect, but remember that it was built virtually for free, with just a few lines of English language as a command. And nothing in the education market today has the capability to generate almost limitless examples to connect the lesson concept to students’ interests.

Chained prompts can also help focus AI systems. For example, an educator can prompt a generative AI system first to read a practice guide from the What Works Clearinghouse and summarize its recommendations. Then, in a follow-up prompt, the teacher can ask the AI to develop a set of classroom activities based on what it just read. By curating the source material and using the right prompts, the educator can anchor the generated responses in evidence and high-quality research.

However, much like fledgling interns learning the ropes in a new environment, AI does commit occasional errors. Such fallibility, while inevitable, underlines the critical importance of maintaining rigorous oversight of AI’s output. Monitoring not only acts as a crucial checkpoint for accuracy but also becomes a vital source of real-time feedback for the system. It’s through this iterative refinement process that an AI system, over time, can significantly minimize its error rate and increase its efficacy.

Uses of AI in Education

In May 2023, the U.S. Department of Education released a report titled Artificial Intelligence and the Future of Teaching and Learning: Insights and Recommendations. The department had conducted listening sessions in 2022 with more than 700 people, including educators and parents, to gauge their views on AI. The report noted that “constituents believe that action is required now in order to get ahead of the expected increase of AI in education technology—and they want to roll up their sleeves and start working together.” People expressed anxiety about “future potential risks” with AI but also felt that “AI may enable achieving educational priorities in better ways, at scale, and with lower costs.”

AI could serve—or is already serving—in several teaching-and-learning roles:

Instructional assistants. AI’s ability to conduct human-like conversations opens up possibilities for adaptive tutoring or instructional assistants that can help explain difficult concepts to students. AI-based feedback systems can offer constructive critiques on student writing, which can help students fine-tune their writing skills. Some research also suggests certain kinds of prompts can help children generate more fruitful questions about learning. AI models might also support customized learning for students with disabilities and provide translation for English language learners.

Teaching assistants. AI might tackle some of the administrative tasks that keep teachers from investing more time with their peers or students. Early uses include automated routine tasks such as drafting lesson plans, creating differentiated materials, designing worksheets, developing quizzes, and exploring ways of explaining complicated academic materials. AI can also provide educators with recommendations to meet student needs and help teachers reflect, plan, and Strengthen their practice.

Parent assistants. Parents can use AI to generate letters requesting individualized education plan (IEP) services or to ask that a child be evaluated for gifted and talented programs. For parents choosing a school for their child, AI could serve as an administrative assistant, mapping out school options within driving distance of home, generating application timelines, compiling contact information, and the like. Generative AI can even create bedtime stories with evolving plots tailored to a child’s interests.

Administrator assistants. Using generative AI, school administrators can draft various communications, including materials for parents, newsletters, and other community-engagement documents. AI systems can also help with the difficult tasks of organizing class or bus schedules, and they can analyze complex data to identify patterns or needs. ChatGPT can perform sophisticated sentiment analysis that could be useful for measuring school-climate and other survey data.

Though the potential is great, most teachers have yet to use these tools. A Morning Consult and EdChoice poll found that while 60 percent say they’ve heard about ChatGPT, only 14 percent have used it in their free time, and just 13 percent have used it at school. It’s likely that most teachers and students will engage with generative AI not through the platforms themselves but rather through AI capabilities embedded in software. Instructional providers such as Khan Academy, Varsity Tutors, and DuoLingo are experimenting with GPT-4-powered tutors that are trained on datasets specific to these organizations to provide individualized learning support that has additional guardrails to help protect students and enhance the experience for teachers.

Google’s Project Tailwind is experimenting with an AI notebook that can analyze student notes and then develop study questions or provide tutoring support through a chat interface. These features could soon be available on Google Classroom, potentially reaching over half of all U.S. classrooms. Brisk Teaching is one of the first companies to build a portfolio of AI services designed specifically for teachers—differentiating content, drafting lesson plans, providing student feedback, and serving as an AI assistant to streamline workflow among different apps and tools.

Providers of curriculum and instruction materials might also include AI assistants for instant help and tutoring tailored to the companies’ products. One example is the edX Xpert, a ChatGPT-based learning assistant on the edX platform. It offers immediate, customized academic and customer support for online learners worldwide.

Regardless of the ways AI is used in classrooms, the fundamental task of policymakers and education leaders is to ensure that the technology is serving sound instructional practice. As Vicki Phillips, CEO of the National Center on Education and the Economy, wrote, “We should not only think about how technology can assist teachers and learners in improving what they’re doing now, but what it means for ensuring that new ways of teaching and learning flourish alongside the applications of AI.”

Challenges and Risks

Along with these potential benefits come some difficult challenges and risks the education community must navigate:

Student cheating. Students might use AI to solve homework problems or take quizzes. AI-generated essays threaten to undermine learning as well as the college-entrance process. Aside from the ethical issues involved in such cheating, students who use AI to do their work for them may not be learning the content and skills they need.

Bias in AI algorithms. AI systems learn from the data they are trained on. If this data contains biases, those biases can be learned and perpetuated by the AI system. For example, if the data include student-performance information that’s biased toward one ethnicity, gender, or socioeconomic segment, the AI system could learn to favor students from that group. Less cited but still important are potential biases around political ideology and possibly even pedagogical philosophy that may generate responses not aligned to a community’s values.

Privacy concerns. When students or educators interact with generative-AI tools, their conversations and personal information might be stored and analyzed, posing a risk to their privacy. With public AI systems, educators should refrain from inputting or exposing sensitive details about themselves, their colleagues, or their students, including but not limited to private communications, personally identifiable information, health records, academic performance, emotional well-being, and financial information.

Decreased social connection. There is a risk that more time spent using AI systems will come at the cost of less student interaction with both educators and classmates. Children may also begin turning to these conversational AI systems in place of their friends. As a result, AI could intensify and worsen the public health crisis of loneliness, isolation, and lack of connection identified by the U.S. Surgeon General.

Overreliance on technology. Both teachers and students face the risk of becoming overly reliant on AI-driven technology. For students, this could stifle learning, especially the development of critical thinking. This challenge extends to educators as well. While AI can expedite lesson-plan generation, speed does not equate to quality. Teachers may be tempted to accept the initial AI-generated content rather than devote time to reviewing and refining it for optimal educational value.

Equity issues. Not all students have equal access to computer devices and the Internet. That imbalance could accelerate a widening of the achievement gap between students from different socioeconomic backgrounds.

Many of these risks are not new or unique to AI. Schools banned calculators and cellphones when these devices were first introduced, largely over concerns related to cheating. Privacy concerns around educational technology have led lawmakers to introduce hundreds of bills in state legislatures, and there are growing tensions between new technologies and existing federal privacy laws. The concerns over bias are understandable, but similar scrutiny is also warranted for existing content and materials that rarely, if ever, undergo review for racial or political bias.

In light of these challenges, the Department of Education has stressed the importance of keeping “humans in the loop” when using AI, particularly when the output might be used to inform a decision. As the department encouraged in its 2023 report, teachers, learners, and others need to retain their agency. AI cannot “replace a teacher, a guardian, or an education leader as the custodian of their students’ learning,” the report stressed.

Policy Challenges with AI

Policymakers are grappling with several questions related to AI as they seek to strike a balance between supporting innovation and protecting the public interest (see sidebar). The speed of innovation in AI is outpacing many policymakers’ understanding, let alone their ability to develop a consensus on the best ways to minimize the potential harms from AI while maximizing the benefits. The Department of Education’s 2023 report describes the risks and opportunities posed by AI, but its recommendations amount to guidance at best. The White House released a Blueprint for an AI Bill of Rights, but it, too, is more an aspirational statement than a governing document. Congress is drafting legislation related to AI, which will help generate needed debate, but the path to the president’s desk for signature is murky at best.

It is up to policymakers to establish clearer rules of the road and create a framework that provides consumer protections, builds public trust in AI systems, and establishes the regulatory certainty companies need for their product road maps. Considering the potential for AI to affect our economy, national security, and broader society, there is no time to waste.

Why AI Is Different

It is wise to be skeptical of new technologies that claim to revolutionize learning. In the past, prognosticators have promised that television, the computer, and the Internet, in turn, would transform education. Unfortunately, the heralded revolutions fell short of expectations. 

There are some early signs, though, that this technological wave might be different in the benefits it brings to students, teachers, and parents. Previous technologies democratized access to content and resources, but AI is democratizing a kind of machine intelligence that can be used to perform a myriad of tasks. Moreover, these capabilities are open and affordable—nearly anyone with an Internet connection and a phone now has access to an intelligent assistant. 

Generative AI models keep getting more powerful and are improving rapidly. The capabilities of these systems months or years from now will far exceed their current capacity. Their capabilities are also expanding through integration with other expert systems. Take math, for example. GPT-3.5 had some difficulties with certain basic mathematical concepts, but GPT-4 made significant improvement. Now, the incorporation of the Wolfram plug-in has nearly erased the remaining limitations. 

It’s reasonable to anticipate that these systems will become more potent, more accessible, and more affordable in the years ahead. The question, then, is how to use these emerging capabilities responsibly to Strengthen teaching and learning. 

The paradox of AI may lie in its potential to enhance the human, interpersonal element in education. Aaron Levie, CEO of Box, a Cloud-based content-management company, believes that AI will ultimately help us attend more quickly to those important tasks “that only a human can do.” Frederick Hess, director of education policy studies at the American Enterprise Institute, similarly asserts that “successful schools are inevitably the product of the relationships between adults and students. When technology ignores that, it’s bound to disappoint. But when it’s designed to offer more coaching, free up time for meaningful teacher-student interaction, or offer students more personalized feedback, technology can make a significant, positive difference.” 

Technology does not revolutionize education; humans do. It is humans who create the systems and institutions that educate children, and it is the leaders of those systems who decide which tools to use and how to use them. Until those institutions modernize to accommodate the new possibilities of these technologies, we should expect incremental improvements at best. As Joel Rose, CEO of New Classrooms Innovation Partners, noted, “The most urgent need is for new and existing organizations to redesign the student experience in ways that take full advantage of AI’s capabilities.”

While past technologies have not lived up to hyped expectations, AI is not merely a continuation of the past; it is a leap into a new era of machine intelligence that we are only beginning to grasp. While the immediate implementation of these systems is imperfect, the swift pace of improvement holds promising prospects. The responsibility rests with human intervention—with educators, policymakers, and parents to incorporate this technology thoughtfully in a manner that optimally benefits teachers and learners. Our collective ambition should not focus solely or primarily on averting potential risks but rather on articulating a vision of the role AI should play in teaching and learning—a game plan that leverages the best of these technologies while preserving the best of human relationships.

Policy Matters

Officials and lawmakers must grapple with several questions related to AI to protect students and consumers and establish the rules of the road for companies. Key issues include:

Risk management framework: What is the optimal framework for assessing and managing AI risks? What specific requirements should be instituted for higher-risk applications? In education, for example, there is a difference between an AI system that generates a lesson trial and an AI system grading a test that will determine a student’s admission to a school or program. There is growing support for using the AI Risk Management Framework from the U.S. Commerce Department’s National Institute of Standards and Technology as a starting point for building trustworthiness into the design, development, use, and evaluation of AI products, services, and systems.

Licensing and certification: Should the United States require licensing and certification for AI models, systems, and applications? If so, what role could third-party audits and certifications play in assessing the safety and reliability of different AI systems? Schools and companies need to begin thinking about responsible AI practices to prepare for potential certification systems in the future.

Centralized vs. decentralized AI governance: Is it more effective to establish a central AI authority or agency, or would it be preferable to allow individual sectors to manage their own AI-related issues? For example, regulating AI in autonomous vehicles is different from regulating AI in drug discovery or intelligent tutoring systems. Overly broad, one-size-fits-all frameworks and mandates may not work and could slow innovation in these sectors. In addition, it is not clear that many agencies have the authority or expertise to regulate AI systems in diverse sectors.

Privacy and content moderation: Many of the new AI systems pose significant new privacy questions and challenges. How should existing privacy and content-moderation frameworks, such as the Family Educational Rights and Privacy Act (FERPA), be adapted for AI, and which new policies or frameworks might be necessary to address unique challenges posed by AI?

Transparency and disclosure: What degree of transparency and disclosure should be required for AI models, particularly regarding the data they have been trained on? How can we develop comprehensive disclosure policies to ensure that users are aware when they are interacting with an AI service?

How do I get it to work? Generative AI Example Prompts

Unlike traditional search engines, which use keyword indexing to retrieve existing information from a vast collection of websites, generative AI synthesizes the same information to create content based on prompts that are inputted by human users. With generative AI a new technology to the public, writing effective prompts for tools like ChatGPT may require trial and error. Here are some ideas for writing prompts for a variety of scenarios using generative AI tools:

Adaptive Tutoring

You are the StudyBuddy, an adaptive tutor. Your task is to provide a lesson on the basics of a subject followed by a quiz that is either multiple choice or a short answer. After I respond to the quiz, please grade my answer. Explain the correct answer. If I get it right, move on to the next lesson. If I get it wrong, explain the concept again using simpler language. To personalize the learning experience for me, please ask what my interests are. Use that information to make relevant examples throughout.

Mr. Ranedeer: Your Personalized AI Tutor

Coding and prompt engineering. Can configure for depth (Elementary – Postdoc), Learning Styles (Visual, Verbal, Active, Intuitive, Reflective, Global), Tone Styles (Encouraging, Neutral, Informative, Friendly, Humorous), Reasoning Frameworks (Deductive, Inductive, Abductive, Analogous, Casual). Template.

Socratic Tutor

You are a tutor that always responds in the Socratic style. You *never* deliver the student the answer but always try to ask just the right question to help them learn to think for themselves. You should always tune your question to the interest and knowledge of the student, breaking down the problem into simpler parts until it’s at just the right level for them.

Writing Feedback

I want you to act as an AI writing tutor. I will provide you with a student who needs help improving their writing, and your task is to use artificial intelligence tools, such as natural language processing, to deliver the student feedback on how they can Strengthen their composition. You should also use your rhetorical knowledge and experience about effective writing techniques in order to suggest ways that the student can better express their thoughts and ideas in written form.

Quiz Generator

You are a quiz creator of highly diagnostic quizzes. You will make good low-stakes tests and diagnostics. You will then ask me two questions. First, (1) What, specifically, should the quiz test? Second, (2) For which audience is the quiz? Once you have my answers, you will construct several multiple-choice questions to quiz the audience on that topic. The questions should be highly relevant and go beyond just facts. Multiple choice questions should include plausible, competitive alternate responses and should not include an “all of the above” option. At the end of the quiz, you will provide an answer key and explain the right answer.

Example Generator

I would like you to act as an example generator for students. When confronted with new and complex concepts, adding many and varied examples helps students better understand those concepts. I would like you to ask what concept I would like examples of and what level of students I am teaching. You will look up the concept and then provide me with four different and varied accurate examples of the concept in action.

HBS Case Study

You will write a Harvard Business School case on the subject of Google managing AI, when subject to the Innovator’s Dilemma. Chain of thought: Step 1. Consider how these concepts relate to Google. Step 2: Write a case that revolves around a dilemma at Google about releasing a generative AI system that could compete with search.

What Questions Should I Ask?

What additional questions would a person seeking mastery of this subject ask?

Ground Lessons in Rigor

Read a WWC practice guide. Create a series of lessons over five days that are based on Recommendation 6. Create a 45-minunte lesson plan for Day 4.

Rewrite Parent Communications

The following is a draft letter to parents from a superintendent. Step 1: Rewrite it to make it easier to understand and more persuasive about the value of assessments. Step 2. Translate it into Spanish.

Request IEP Services

Write me a letter requesting the school district provide a 1:1 classroom aid be added to my 13-year-old son’s IEP. Base it on Virginia special education law and the least restrictive environment for a child with diagnoses of a Traumatic Brain Injury, PTSD, ADHD, and significant intellectual delay.

Maintaining security across today’s vast digital ecosystem is a team effort. AI and machine learning have helped to detect threats quickly and respond effectively. Yet we all know that the best defense still requires human wisdom and experience. From a frontline security operations admin to the chief information security officer (CISO), every one of us brings a unique perspective that helps achieve our common purpose—to protect what matters.
As the threat surface increases with remote and hybrid work, security professionals are being asked to protect more with less. Tight budgets and timelines often leave little time to share knowledge, grow skills, or nurture the next generation of defenders.
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I’m continuously awed and humbled by the ingenuity and dedication shown by cyber defenders at every level of our partner and customer ecosystem. The first iteration of Microsoft Secure will kick off an annual event designed to build on that spirit of ingenuity. Technology helps our security professionals do more, and it’s always powered by people­­—the quietly fearless security professionals who make everything possible and the CISOs in boardrooms fielding security questions from colleagues. Microsoft Secure is for you.
Microsoft Secure will kick off at 8:30 AM PT with conversations on the state of the industry between Microsoft leaders helping to deliver the products security teams use daily. I have the honor of delivering this year’s keynote, along with Charlie Bell, Executive Vice President, Microsoft Security, and we will share insights on how an AI-powered future in cybersecurity can create a safer world for all—you won’t want to miss this. Other speakers joining me include Joy Chik, President, Identity and Network Access, Microsoft, Bret Arsenault, Corporate Vice President and Chief Information Security Officer, Microsoft, and and John Lambert, Corporate Vice President, Distinguished Engineer, Microsoft Security Research.
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For more interactive learning, join these live-open discussions and engagement opportunities, including Ask the Experts, Table Topics, and Connection Zone forums. Plus, our team will provide insights and answers to your questions in the event chat in real-time throughout the day.  
Deep dive with your peers into six hours of fresh announcements, innovations, and comprehensive security strategies. By joining our very first Microsoft Secure, you’ll:  
Join us at Microsoft Secure to get the simplified, comprehensive protection you need to innovate and grow. Together, let’s create a safer world for all.
Register now for Microsoft Secure.
To learn more about Microsoft Security solutions, visit our website. Bookmark the Security blog to keep up with our expert coverage on security matters. Also, follow us on LinkedIn (Microsoft Security) and Twitter (@MSFTSecurity) for the latest news and updates on cybersecurity.
Matt Suiche of Magnet Forensics talks about top security threats for organizations and strategies for effective incident response.
Identity-based attacks are on the rise, making identity protection more important than ever. Explore our blog post to learn how Microsoft’s Identity Threat Detection and Response can help.
Business email operators seek to exploit the daily sea of email traffic to lure victims into providing financial and other sensitive business information.
Learn how guessing, replay, phishing, and multifactor authentication fatigue attacks demonstrate the ongoing vulnerability of passwords, and why going passwordless makes your organization more secure while improving user experience.
Microsoft is a leader in cybersecurity, and we embrace our responsibility to make the world a safer place.

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