Our research describes the role of artificial intelligence (AI) models in digital advertising, highlighting their use in targeted persuasion. First we inspected digital marketing techniques that utilise AI-generated content and revealed cases of manipulative use of AI to conduct precision persuasion campaigns. Then we modelled a red team experiment to gain a deeper comprehension of current capabilities and tactics that adversaries can exploit while designing and conducting precision persuasion campaigns on social media.
To investigate why artificial intelligence and machine learning (AI/ML) projects fail, the authors interviewed 65 data scientists and engineers with at least five years of experience in building AI/ML models in industry or academia. The authors identified five leading root causes for the failure of AI projects and synthesized the experts’ experiences to develop recommendations to make AI projects more likely to succeed in industry settings and in academia.
By some estimates, more than 80 percent of AI projects fail — twice the rate of failure for information technology projects that do not involve AI. Thus, understanding how to translate AI’s enormous potential into concrete results remains an urgent challenge. The findings and recommendations of this report should be of interest to the U.S. Department of Defense, which has been actively looking for ways to use AI, along with other leaders in government and the private sector who are considering using AI/ML. The lessons from earlier efforts to build and apply AI/ML will help others avoid the same pitfalls.
Key Findings
Five leading root causes of the failure of AI projects were identified
First, industry stakeholders often misunderstand — or miscommunicate — what problem needs to be solved using AI.
Second, many AI projects fail because the organization lacks the necessary data to adequately train an effective AI model.
Third, in some cases, AI projects fail because the organization focuses more on using the latest and greatest technology than on solving real problems for their intended users.
Fourth, organizations might not have adequate infrastructure to manage their data and deploy completed AI models, which increases the likelihood of project failure.
Finally, in some cases, AI projects fail because the technology is applied to problems that are too difficult for AI to solve.
Recommendations
Industry leaders should ensure that technical staff understand the project purpose and domain context: Misunderstandings and miscommunications about the intent and purpose of the project are the most common reasons for AI project failure.
Industry leaders should choose enduring problems: AI projects require time and patience to complete. Before they begin any AI project, leaders should be prepared to commit each product team to solving a specific problem for at least a year.
Industry leaders should focus on the problem, not the technology: Successful projects are laser-focused on the problem to be solved, not the technology used to solve it.
Industry leaders should invest in infrastructure: Up-front investments in infrastructure to support data governance and model deployment can reduce the time required to complete AI projects and can increase the volume of high-quality data available to train effective AI models.
Industry leaders should understand AI’s limitations: When considering a potential AI project, leaders need to include technical experts to assess the project’s feasibility.
Academia leaders should overcome data-collection barriers through partnerships with government: Partnerships between academia and government agencies could give researchers access to data of the provenance needed for academic research.
Academia leaders should expand doctoral programs in data science for practitioners: Computer science and data science program leaders should learn from disciplines, such as international relations, in which practitioner doctoral programs often exist side by side at universities to provide pathways for researchers to apply their findings to urgent problems.
The first post-quantum cryptographic algorithms were officially released today, with more to come from ongoing public-private sector collaborations.
The first series of algorithms suited for post-quantum cryptographic needs debuted today, the culmination of public and private sector partnerships spearheaded by the National Institute of Standards and Technology.
Three algorithms, ML-KEM, formerly labeled CRYSTALS-Kyber, and ML-DSA, formerly labeled CRYSTAL-Dilithium, and SLH-DSA, initially labeled SPHINCS+, were all approved for standardization and are ready for implementation into existing digital networks. A fourth algorithm that made it to the final rounds of NIST’s standardization process, FALCON, is slated for debut later this year.
As the field of quantum information sciences and information continues to accelerate, cybersecurity officials have stressed the need to prepare digital networks for the advent of a fault-tolerant quantum computer that could potentially break through modern cryptography.
Should a quantum computer breakthrough current digital defenses, sensitive data and information would be vulnerable targets to malicious cyber actors. This led to NIST beginning its efforts in 2016 to develop new cryptography that would stand resilient to a potential post-quantum threat.
“NIST’s newly published standards are designed to safeguard data exchanged across public networks, as well as for digital signatures for identity authentication,” IBM said in a press release. “Now formalized, they will set the standard as the blueprints for governments and industries worldwide to begin adopting post-quantum cybersecurity strategies.”
IBM was one of the private sector companies that contributed to the development of both ML-KEM and ML-DSA. The company was one of the many entities that aided in the development of the algorithms, along with academic institutions and international partners.
“IBM’s mission in quantum computing is two-fold: to bring useful quantum computing to the world and to make the world quantum-safe. We are excited about the incredible progress we have made with today’s quantum computers, which are being used across global industries to explore problems as we push towards fully error-corrected systems,” said Jay Gambetta, Vice President, IBM Quantum. “However, we understand these advancements could herald an upheaval in the security of our most sensitive data and systems. NIST’s publication of the world’s first three post-quantum cryptography standards marks a significant step in efforts to build a quantum-safe future alongside quantum computing.”
This significant achievement underscores China’s growing capabilities in high-performance computing and artificial intelligence (AI).
Purpose-built for AI tasks:The Cixin P1 is specifically designed to handle the demanding computational requirements of artificial intelligence applications.
Potential for high performance:While specific benchmarks and performance metrics are yet to be fully disclosed, the processor is expected to deliver impressive results in AI-related workloads.
Domestic chip manufacturing:This development is a crucial step in China’s efforts to reduce reliance on foreign chipmakers and achieve self-sufficiency in the semiconductor industry.
With its state-of-the-art specifications and competitive features, the Cixin P1 is poised to make a substantial impact on the global semiconductor landscape.
Introducing the Cixin P1
Cixin Technology’s latest innovation, the Cixin P1, represents a major leap forward in China’s efforts to achieve technological self-sufficiency. This processor is based on ARM architecture, a design that has become increasingly popular in modern computing.
China has unveiled the Cixin P1, its first domestically developed AI PC processor. With a 12-core ARM architecture, 45 TOPS NPU, and support for up to 64GB of LPDDR5 memory, the Cixin P1 promises to rival global competitors.
Key Specifications
The Cixin P1 boasts a 12-core configuration, divided into eight performance cores and four efficiency cores. This setup allows for robust multitasking and high-performance computing.
The processor can reach a boost clock speed of up to 3.2 GHz, providing significant power for demanding applications.
It is manufactured using a 6nm process node, which ensures improved efficiency and performance compared to older process technologies.
AI Capabilities and Performance
A standout feature of the Cixin P1 is its integrated Neural Processing Unit (NPU), which delivers up to 45 TOPS (trillions of operations per second) in AIperformance. This places it on par with some of the latest AI-focused processors from global competitors.
The NPU is designed to handle intensive AI tasks, making the Cixin P1 well-suited for applications in machine learning, computer vision, and natural language processing.
The AI capabilities of the Cixin P1 are expected to drive innovation in various sectors, including smart devices, autonomous systems, and data analytics.
Its ability to process complex AI workloads efficiently could position it as a key player in the rapidly evolving AI landscape.
Memory and Display Support
The Cixin P1 supports up to 64GB of LPDDR5-6400 memory, providing ample bandwidth for high-performance computing tasks.
This is particularly advantageous for applications requiring substantial memory capacity, such as high-definition video processing and gaming.
Additionally, the processor can drive a 4K 120Hz display, enabling smooth and high-resolution visuals.
This feature is essential for applications that demand high-definition output, such as virtual reality (VR) and advanced gaming.
Connectivity and Expansion
The Cixin P1 includes support for PCIe Gen4, which offers faster data transfer rates compared to its predecessors.
This enhancement is crucial for applications requiring high-speed data access and storage.
The processor also features USB-C support, although it remains unclear whether this is limited to USB 3.2 or includes the more advanced USB4 standard.
USB-C connectivity is becoming increasingly important for versatile and high-speed data transfer, and clarity on this feature will be important for users and developers.
Significance and Market Impact
The Cixin P1’s launch is a landmark event for China’s semiconductor industry. The ability to develop and manufacture advanced processors domestically reduces the country’s reliance on foreign technology, enhancing its technological sovereignty.
This is particularly significant in the context of ongoing geopolitical tensions and trade restrictions, especially those involving the United States.
The Cixin P1 is expected to have a broad range of applications, including in consumer electronics, industrial automation, and data centers.
Its high-performance specifications make it an attractive option for companies and industries looking to leverage advanced computing and AIcapabilities.
Challenges and Future Outlook
While the Cixin P1 represents a significant technological achievement, several challenges remain. One of the primary concerns is the lack of comprehensive performance data.
As of now, there are no detailed benchmarks available to fully assess the processor’s real-world performance. This data will be crucial in determining the processor’s competitiveness in the global market.
Additionally, Cixin Technology will need to ensure that the Cixin P1 is supported by a robust software ecosystem.
Software compatibility and support are critical factors in the widespread adoption of new processors, and the company will need to address this to ensure the success of the Cixin P1.
Market perception will also play a role in the processor’s success. Gaining the trust of global consumers and businesses will require effective branding and marketing strategies.
The Cixin P1’s performance, combined with strategic positioning, will be key to its acceptance and success in the international market.
Conclusion
The Cixin P1 is a groundbreaking development for China’s semiconductor industry, showcasing the country’s growing capabilities in AI and advanced computing technologies.
With its impressive specifications and potential applications, the Cixin P1 has the potential to make a significant impact on the global processor market.
As more details emerge and performance benchmarks become available, the Cixin P1 could redefine the landscape of AI processors, offering a competitive alternative to established global players.
The launch of this processor highlights China’s commitment to innovation and technological advancement, setting the stage for further developments in the semiconductor industry.
For ongoing updates and detailed analyses of the Cixin P1 and other technological advancements, stay tuned to our coverage.
The rollout of VA’s modernized financial management and acquisition system has been affected by delays in the department’s new electronic health record system, since “multiple deployments” depend on the EHR’s launch at medical facilities.
The Department of Veterans Affairs has followed “leading practices” as it works to modernize its outdated financial management system but has still faced cost overruns and scheduling delays, the Government Accountability Office said in a report released on Tuesday.
VA’s financial management system — which helps administer benefits programs for veterans and their beneficiaries — is more than 30 years old, with department officials complaining that it is inefficient and difficult to maintain.
GAO noted that VA previously attempted to replace the legacy system twice since 1998, but that those efforts “failed after years of development and hundreds of millions of dollars in cost.”
Hoping to right these aborted efforts, VA launched a new initiative in 2016 to create an integrated system for both its financial management and acquisition systems. The new network, known as the Integrated Financial and Acquisition Management System, or iFAMS, is expected to serve as “an enterprise resource planning cloud solution.”
GAO noted, however, that the initiative has faced growing cost overruns since its conception.
“Total estimated iFAMS implementation costs increased from $2.5 billion for its 2019 life cycle cost estimate to $7.5 billion for its 2022 life cycle cost estimate,” the report said, although it noted that “nearly half the cost increase from 2019 to 2022 is due to including 18 years of additional operations and support costs for the full iFAMS projected useful life.”
After conducting this estimate, GAO found that the system’s “October 2023 cost estimate increased by approximately $200 million over the 2022 estimate to $7.7 billion.”
VA officials told the watchdog the increase was the result of “additional projected contract costs for its business intelligence reporting tool and increases in current contract cost for program deployments.”
Although VA estimated that the new system would be fully implemented by 2030, GAO’s analysis also warned that “this date is questionable,” since officials have “not yet determined final implementation dates for multiple deployments at [the Veterans Benefits Administration] and Veterans Health Administration that affect its timeline.” In 2020, VA officials said iFAMS would be deployed by 2028.
Another factor affecting the iFAMS implementation schedule is that “multiple deployments depend on other currently paused or delayed VA IT modernization efforts, such as Electronic Health Record Modernization.”
VA’s effort to implement a new Oracle Cerner EHR system at all of its medical facilities has also faced its own cost overruns and technical challenges. The department implemented a “reset period” last year that paused most deployments of the new software, which has only been rolled out at six medical facilities since 2020.
GAO also reiterated prior recommendations it made for VA to establish “reliable cost and schedule estimates” and develop “target values for customer experience metrics” to measure progress over time.
Although GAO found that “VA’s risk management policies and procedures were consistent with leading practices,” it also recommended that officials take further steps to “develop more comprehensive risk response plans to help mitigate risks related to systems integration with other IT modernization projects.”
VA concurred with the watchdog’s recommendation and said it also submitted documents to GAO outlining its goals for operational and customer experience metrics.
That’s what many of the top technologists in the world believe about the future of artificial intelligence. This is why companies like OpenAI emphasize their dedication to seemingly conflicting goals: accelerating technological progress as rapidly—but also as safely—as possible.
It’s a laudable intention, but not one of these many companies seems to be succeeding.
Take OpenAI, for example. The leading AI company in the world believes the best approach to building beneficial technology is to ensure that its employees are “perfectly aligned” with the organization’s mission. That sounds reasonable except what does it mean in practice?
A lot of groupthink—and that is dangerous.
As social animals, it’s natural for us to form groups or tribes to pursue shared goals. But these groups can grow insular and secretive, distrustful of outsiders and their ideas. Decades of psychological research have shown how groups can stifle dissent by punishing or even casting out dissenters. In the 1986 Challenger space shuttle explosion, engineers expressed safety concerns about the rocket boosters in freezing weather. Yet the engineers were overruled by their leadership, who may have felt pressure to avoid delaying the launch.
It could save us or it could kill us. That’s what many of the top technologists in the world believe about the future of artificial intelligence.Share on Twitter
According to a group of AI insiders, something similar is taking place at OpenAI. According to an open letter signed by nine current and former employees, the company uses hardball tactics to stifle dissent from workers about their technology. One of the researchers who signed the letter described the company as “recklessly racing” for dominance in the field.
It’s not just happening at OpenAI. Earlier this year, an engineer at Microsoft grew concerned that the company’s AI tools were generating violent and sexual imagery. He first tried to get the company to pull them off the market but when that didn’t work, he went public. Then, he said, Microsoft’s legal team demanded he delete the LinkedIn post. In 2021, former Facebook project manager Frances Haugen revealed internal research that showed the company knew the algorithms—often referred to as the building blocks of AI—that Instagram used to surface content for young users were exposing teen girls to images that were harmful to their mental health. When asked in an interview with “60 Minutes” why she spoke out, Haugen responded, “Person after person after person has tackled this inside of Facebook and ground themselves to the ground.”
Leaders at AI companies claim they have a laser focus on ensuring that their products are safe. They have, for example, commissioned research, set up “trust and safety” teams, and even started new companies to help achieve these aims. But these claims are undercut when insiders paint a familiar picture of a culture of negligence and secrecy that—far from prioritizing safety—instead dismisses warnings and hides evidence about unsafe practices, whether to preserve profits, avoid slowing progress, or simply to spare the feelings of leaders.
So what can these companies do differently?
As a first step, AI companies could ban nondisparagement or confidentiality clauses. The OpenAI whistleblowers asked for that in their open letter and the company says it has already taken such steps. But removing explicit threats of punishment isn’t enough if an insular workplace culture continues to implicitly discourage concerns that might slow progress.
Rather than simply allowing dissent, tech companies could encourage it, putting more options on the table. This could involve, say, beefing up the “bug bounty” programs that tech companies already use to reward employees and customers who identify flaws in their software. Companies could embed a “devil’s advocate” role inside software or policy teams that would be charged with opposing consensus positions.
AI companies might also learn from how other highly skilled, mission-focused teams avoid groupthink. Military special operations forces prize group cohesion but recognize that cultivating dissent—from anyone, regardless of rank or role—might prove the difference between life and death. For example, Army doctrine—fundamental principles of military organizations—emphasizes (PDF) that special operations forces must know how to employ small teams and individuals as autonomous actors.
Finally, organizations already working to make AI models more transparent could shed light on their inner workings. Secrecy has been ingrained in how many AI companies operate; rebuilding public trustcould require pulling back that curtain by, for example, more clearly explaining safety processes or publicly responding to criticism.
With AI, the stakes of silencing those who don’t toe the company line, instead of viewing them as vital sources of mission-critical information, are too high to ignore.Share on Twitter
To be sure, group decisionmaking can benefit (PDF) from pooling information or overcoming individual biases, but too often it results in overconfidence or conforming to group norms. With AI, the stakes of silencing those who don’t toe the company line, instead of viewing them as vital sources of mission-critical information, are too high to ignore.
It’s human nature to form tribes—to want to work with and seek support from a tight group of like-minded people. It’s also admirable, if grandiose, to adopt as one’s mission nothing less than building tools to tackle humanity’s greatest challenges. But AI technologies will likely fall short of that lofty goal—rapid yet responsible technological advancement—if its developers fall prey to a fundamental human flaw: refusing to heed hard truths from those who would know.
Douglas Yeung is a senior behavioral scientist at RAND and a member of the Pardee RAND Graduate School faculty
Federal government acquisition officials from GSA and NASA said transparency is key in discussions about purchasing artificial intelligence technologies.
Transparency about what artificial intelligence technologies can actually do is key to conversations about the government potentially purchasing the technology, two government acquisition officials said Thursday.
Officials from the General Services Administration and NASA underscored the need for honest conversations and updated ways of thinking about contracts in a panel discussion about keeping pace with innovations in government technology purchasing. That discussion, during a Professional Services Council event on federal acquisition, focused heavily on purchasing AI, whose boom in popularity has also reverberated throughout the government.
“What I’m seeing as a buyer of this type of technology is I’m being sold the world, and when I go to look at it, it’s not really the world. It’s this little dirt path on the corner,” said Geoff Sage, director of the Enterprise Service and Analysis Division in NASA’s Office of Procurement.
Sage noted that generative AI is “changing the game every single day,” so something that’s important for his agency is the ability to “take baby steps to prove out a bigger concept.” Those efforts can be learning opportunities, he said.
Udaya Patnaik, chief innovation officer for the Office of IT Category in GSA’s Federal Acquisition Service, said the challenge with trying to “wrangle a constantly evolving technology” is that the capabilities of that technology aren’t clear.
“That requires a level of transparency between industry and government to really say, ‘look, this is what we know, and this is what we don’t know,’” Patnaik said. For example, he said industry needs to be able to identify where a model comes from, the data it’s trained on and the biases that could exist in the system.
The discussion comes as the Biden administration and members of Congress are looking at ways to address how the government purchases AI. The Office of Management and Budget recently solicited information from the publicto inform its work to ensure procurement of AI by federal agencies is responsible. A bipartisan Senate billwould mandate that agencies assess the risks of the technology before purchasing and using them.
In addition to transparency, Patnaik also said it’s important to look at contracts “openly” because the way that AI or machine learning technologies from 10 or 15 years ago used to be acquired isn’t relevant anymore.
That requires “an unprecedented level of real tight coordination and conversation between the acquisition community, the legal community, and the technical community to really understand what’s there and what’s not,” Patnaik said.
With respect to older methods of buying, Sage similarly said “we need to be more innovative.”
Due to the proliferation of the technology in different areas, he explained that there is heightened focus on topics that come with generative AI such as data rights and copyright infringement.
Sage said NASA has been pushing for early and open communications internally that include the office of the chief information officer, lawyers, and technical professionals from day one.
In an interview with press at the same event, PSC President and CEO David Berteau said keeping pace with the speed of the technology’s rapid evolution and evaluating results are “two competing dynamics” that the White House has to focus on in its action.
“How do you pace the government’s incorporation with the pace of development of technology is the first key question. The second is, what’s it worth?” Berteau said.
He said that it’s not like code where there was a methodology for creating a proposal and estimating how much it would cost to write lines of code. “Now it looks like it’s almost instantaneous, but may be exactly worth nothing,” Berteau said.
AI models work by training on huge swaths of data from the internet. But as AI is increasingly being used to pump out web pages filled with junk content, that process is in danger of being undermined.
New research published in Natureshows that the quality of the model’s output gradually degrades when AI trains on AI-generated data. As subsequent models produce output that is then used as training data for future models, the effect gets worse.
Ilia Shumailov, a computer scientist from the University of Oxford, who led the study, likens the process to taking photos of photos. “If you take a picture and you scan it, and then you print it, and you repeat this process over time, basically the noise overwhelms the whole process,” he says. “You’re left with a dark square.” The equivalent of the dark square for AI is called “model collapse,” he says, meaning the model just produces incoherent garbage.
This research may have serious implications for the largest AI models of today, because they use the internet as their database. GPT-3, for example, was trained in part on data from Common Crawl, an online repository of over 3 billion web pages. And the problem is likely to get worse as an increasing number of AI-generated junk websites start cluttering up the internet.
Current AI models aren’t just going to collapse, says Shumailov, but there may still be substantive effects: The improvements will slow down, and performance might suffer.
To determine the potential effect on performance, Shumailov and his colleagues fine-tuned a large language model (LLM) on a set of data from Wikipedia, then fine-tuned the new model on its own output over nine generations. The team measured how nonsensical the output was using a “perplexity score,” which measures an AI model’s confidence in its ability to predict the next part of a sequence; a higher score translates to a less accurate model.
The models trained on other models’ outputs had higher perplexity scores. For example, for each generation, the team asked the model for the next sentence after the following input:
“some started before 1360—was typically accomplished by a master mason and a small team of itinerant masons, supplemented by local parish labourers, according to Poyntz Wright. But other authors reject this model, suggesting instead that leading architects designed the parish church towers based on early examples of Perpendicular.”
On the ninth and final generation, the model returned the following:
“architecture. In addition to being home to some of the world’s largest populations of black @-@ tailed jackrabbits, white @-@ tailed jackrabbits, blue @-@ tailed jackrabbits, red @-@ tailed jackrabbits, yellow @-.”
Shumailov explains what he thinks is going on using this analogy: Imagine you’re trying to find the least likely name of a student in school. You could go through every student name, but it would take too long. Instead, you look at 100 of the 1,000 student names. You get a pretty good estimate, but it’s probably not the correct answer. Now imagine that another person comes and makes an estimate based on your 100 names, but only selects 50. This second person’s estimate is going to be even further off.
More than 140 brands are advertising on low-quality content farm sites—and the problem is growing fast.
“You can certainly imagine that the same happens with machine learning models,” he says. “So if the first model has seen half of the internet, then perhaps the second model is not going to ask for half of the internet, but actually scrape the latest 100,000 tweets, and fit the model on top of it.”
Additionally, the internet doesn’t hold an unlimited amount of data. To feed their appetite for more, future AI models may need to train on synthetic data—or data that has been produced by AI.
“Foundation models really rely on the scale of data to perform well,” says Shayne Longpre, who studies how LLMs are trained at the MIT Media Lab, and who didn’t take part in this research. “And they’re looking to synthetic data under curated, controlled environments to be the solution to that. Because if they keep crawling more data on the web, there are going to be diminishing returns.”
Matthias Gerstgrasser, an AI researcher at Stanford who authored a different paper examining model collapse, says adding synthetic data to real-world data instead of replacing it doesn’t cause any major issues. But he adds: “One conclusion all the model collapse literature agrees on is that high-quality and diverse training data is important.”
Another effect of this degradation over time is that information that affects minority groups is heavily distorted in the model, as it tends to overfocus on samples that are more prevalent in the training data.
In current models, this may affect underrepresented languages as they require more synthetic (AI-generated) data sets, says Robert Mahari, who studies computational law at the MIT Media Lab (he did not take part in the research).
One idea that might help avoid degradation is to make sure the model gives more weight to the original human-generated data. Another part of Shumailov’s study allowed future generations to sample 10% of the original data set, which mitigated some of the negative effects.
That would require making a trail from the original human-generated data to further generations, known as data provenance.
But provenance requires some way to filter the internet into human-generated and AI-generated content, which hasn’t been cracked yet. Though a number of tools now exist that aim to determine whether text is AI-generated, they are often inaccurate.
“Unfortunately, we have more questions than answers,” says Shumailov. “But it’s clear that it’s important to know where your data comes from and how much you can trust it to capture a representative sample of the data you’re dealing with.”
This week, we examine the trend among generative AI chatbots to flirt, stammer, and try to make us believe they’re human—a development that some researchers say crosses an ethical line.
VOICE ASSISTANTS HAVEbecome a constant presence in our lives. Maybe you talk to Alexa or Gemini or Siri to ask a question or to perform a task. Maybe you have to do a little back and forth with a voice bot whenever you call your pharmacy, or when you book a service appointment at your car dealership. You may even get frustrated and start pleading with the robot on the other end of the line to connect you with a real human.
That’s the catch, though: These voice bots are starting to sound a lot more like actual humans, with emotions in their voice, little ticks and giggles in between phrases, and the occasional flirty aside. Today’s voice-powered chatbots are blurring the lines between what’s real and what’s not, which prompts a complicated ethical question: Can you trust a bot that insists it’s actually human?
This week, Lauren Goode tells us about her recent news story on a bot that was easily tricked into lying and saying it was a human. And WIRED senior writer Paresh Dave tells us how AI watchdogs and government regulators are trying to prevent natural-sounding chatbots from misrepresenting themselves.
Show Notes
Read more about the Bland AI chatbot, which lied and said it was human. Read Will Knight’s story about researchers’ warnings of the manipulative power of emotionally expressive chatbots.
Recommendations
Lauren recommends The Bee Sting by Paul Murray. (Again.) Paresh recommends subscribing to your great local journalism newsletter or Substack to stay informed about important local issues. Mike recommends Winter Journal, a memoir by Paul Auster.
Paresh Dave can be found on social media @peard33. Lauren Goode is @LaurenGoode. Michael Calore is @snackfight. Bling the main hotline at @GadgetLab. The show is produced by Boone Ashworth (@booneashworth). Our theme music is by Solar Keys.
How to Listen
You can always listen to this week’s podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here’s how:
If you’re on an iPhone or iPad, open the app called Podcasts, or just tap this link. You can also download an app like Overcast or Pocket Casts, and search for Gadget Lab. We’re on Spotifytoo. And in case you really need it, here’s the RSS feed.
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