A wave of retractions is shaking physics

Recent highly publicized scandals have gotten the physics community worried about its reputation—and its future. Over the last five years, several claims of major breakthroughs in quantum computing and superconducting research, published in prestigious journals, have disintegrated as other researchers found they could not reproduce the blockbuster results. 

Last week, around 50 physicists, scientific journal editors, and emissaries from the National Science Foundation gathered at the University of Pittsburgh to discuss the best way forward.“To be honest, we’ve let it go a little too long,” says physicist Sergey Frolov of the University of Pittsburgh, one of the conference organizers. 

The attendees gathered in the wake of retractions from two prominent research teams. One team, led by physicist Ranga Dias of the University of Rochester, claimed that it had invented the world’s first room temperature superconductor in a 2023 paper in Nature. After independent researchers reviewed the work, a subsequent investigation from Dias’s university found that he had fabricated and falsified his data. Nature retracted the paper in November 2023. Last year, Physical Review Letters retracted a 2021 publication on unusual properties in manganese sulfide that Dias co-authored. 

The other high-profile research team consisted of researchers affiliated with Microsoft working to build a quantum computer. In 2021, Nature retracted the team’s 2018 paper that claimed the creation of a pattern of electrons known as a Majorana particle, a long-sought breakthrough in quantum computing. Independent investigations of that research found that the researchers had cherry-picked their data, thus invalidating their findings. Another less-publicized research team pursuing Majorana particles fell to a similar fate, with Science retracting a 2017 article claiming indirect evidence of the particles in 2022.

In today’s scientific enterprise, scientists perform research and submit the work to editors. The editors assign anonymous referees to review the work, and if the paper passes review, the work becomes part of the accepted scientific record. When researchers do publish bad results, it’s not clear who should be held accountable—the referees who approved the work for publication, the journal editors who published it, or the researchers themselves. “Right now everyone’s kind of throwing the hot potato around,” says materials scientist Rachel Kurchin of Carnegie Mellon University, who attended the Pittsburgh meeting.

Much of the three-day meeting, named the International Conference on Reproducibility in Condensed Matter Physics (a field that encompasses research into various states of matter and why they exhibit certain properties), focused on the basic scientific principle that an experiment and its analysis must yield the same results when repeated. “If you think of research as a product that is paid for by the taxpayer, then reproducibility is the quality assurance department,” Frolov told MIT Technology Review. Reproducibility offers scientists a check on their work, and without it, researchers might waste time and money on fruitless projects based on unreliable prior results, he says. 

In addition to presentations and panel discussions, there was a workshop during which participants split into groups and drafted ideas for guidelines that researchers, journals, and funding agencies could follow to prioritize reproducibility in science. The tone of the proceedings stayed civil and even lighthearted at times. Physicist Vincent Mourik of Forschungszentrum Jülich, a German research institution, showed a photo of a toddler eating spaghetti to illustrate his experience investigating another team’s now-retracted experiment. ​​Occasionally the discussion almost sounded like a couples counseling session, with NSF program director Tomasz Durakiewicz asking a panel of journal editors and a researcher to reflect on their “intimate bond based on trust.”

But researchers did not shy from directly criticizing Nature, Science, and the Physical Review family of journals, all of which sent editors to attend the conference. During a panel, physicist Henry Legg of the University of Basel in Switzerland called out the journal Physical Review B for publishing a paper on a quantum computing device by Microsoft researchers that, for intellectual-property reasons, omitted information required for reproducibility. “It does seem like a step backwards,” Legg said. (Sitting in the audience, Physical Review B editor Victor Vakaryuk said that the paper’s authors had agreed to release “the remaining device parameters” by the end of the year.) 

Journals also tend to “focus on story,” said Legg, which can lead editors to be biased toward experimental results that match theoretical predictions. Jessica Thomas, the executive editor of the American Physical Society, which publishes the Physical Review journals, pushed back on Legg’s assertion. “I don’t think that when editors read papers, they’re thinking about a press release or [telling] an amazing story,” Thomas told MIT Technology Review. “I think they’re looking for really good science.” Describing science through narrative is a necessary part of communication, she says. “We feel a responsibility that science serves humanity, and if humanity can’t understand what’s in our journals, then we have a problem.” 

Frolov, whose independent review with Mourik of the Microsoft work spurred its retraction, said he and Mourik have had to repeatedly e-mail the Microsoft researchers and other involved parties to insist on data. “You have to learn how to be an asshole,” he told MIT Technology Review. “It shouldn’t be this hard.” 

At the meeting, editors pointed out that mistakes, misconduct, and retractions have always been a part of science in practice. “I don’t think that things are worse now than they have been in the past,” says Karl Ziemelis, an editor at Nature.

Ziemelis also emphasized that “retractions are not always bad.” While some retractions occur because of research misconduct, “some retractions are of a much more innocent variety—the authors having made or being informed of an honest mistake, and upon reflection, feel they can no longer stand behind the claims of the paper,” he said while speaking on a panel. Indeed, physicist James Hamlin of the University of Florida, one of the presenters and an independent reviewer of Dias’s work, discussed how he had willingly retracted a 2009 experiment published in Physical Review Letters in 2021 after another researcher’s skepticism prompted him to reanalyze the data. 

What’s new is that “the ease of sharing data has enabled scrutiny to a larger extent than existed before,” says Jelena Stajic, an editor at Science. Journals and researchers need a “more standardized approach to how papers should be written and what needs to be shared in peer review and publication,” she says.

Focusing on the scandals “can be distracting” from systemic problems in reproducibility, says attendee Frank Marsiglio, a physicist at the University of Alberta in Canada. Researchers aren’t required to make unprocessed data readily available for outside scrutiny. When Marsiglio has revisited his own published work from a few years ago, sometimes he’s had trouble recalling how his former self drew those conclusions because he didn’t leave enough documentation. “How is somebody who didn’t write the paper going to be able to understand it?” he says.

Problems can arise when researchers get too excited about their own ideas. “What gets the most attention are cases of fraud or data manipulation, like someone copying and pasting data or editing it by hand,” says conference organizer Brian Skinner, a physicist at Ohio State University. “But I think the much more subtle issue is there are cool ideas that the community wants to confirm, and then we find ways to confirm those things.”

But some researchers may publish bad data for a more straightforward reason. The academic culture, popularly described as “publish or perish,” creates an intense pressure on researchers to deliver results. “It’s not a mystery or pathology why somebody who’s under pressure in their work might misstate things to their supervisor,” said Eugenie Reich, a lawyer who represents scientific whistleblowers, during her talk.

Notably, the conference lacked perspectives from researchers based outside the US, Canada, and Europe, and from researchers at companies. In recent years, academics have flocked to companies such as Google, Microsoft, and smaller startups to do quantum computing research, and they have published their work in Nature, Science, and the Physical Review journals. Frolov says he reached out to researchers from a couple of companies, but “that didn’t work out just because of timing,” he says. He aims to include researchers from that arena in future conversations.

After discussing the problems in the field, conference participants proposed feasible solutions for sharing data to improve reproducibility. They discussed how to persuade the community to view data sharing positively, rather than seeing the demand for it as a sign of distrust. They also brought up the practical challenges of asking graduate students to do even more work by preparing their data for outside scrutiny when it may already take them over five years to complete their degree. Meeting participants aim to publicly release a paper with their suggestions. “I think trust in science will ultimately go up if we establish a robust culture of shareable, reproducible, replicable results,” says Frolov. 

Sophia Chen is a science writer based in Columbus, Ohio. She has written for the society that publishes the Physical Review journals, and for the news section of Nature

Charts: Venture Capital Trends Q1 2024

Global venture funding in the first quarter of 2024 reached $66 billion, a 6% increase from the previous quarter but a 20% decrease from the same period last year. That’s according to data by Crunchbase.

In Q1 2024, the enterprise software category received the highest VC funding among industry sectors per Dealroom, a Netherlands-based data platform for startup intelligence. The energy category is approaching the top three for venture capital raised.

“Frontier” technologies combine scientific breakthroughs with real-world needs. During Q1 2024, generative AI, semiconductors, and drug discovery emerged as the top-funded segments in frontier technology.

In Q1 2024, the United States, China, and the United Kingdom continue to lead in venture capital investments.

New Google AI Overviews Documentation & SEO via @sejournal, @martinibuster

Google published new documentation about their new AI Overviews search feature which summarizes an answer to a search query and links to webpages where more information can be found. The new documentation offers important information about how the new feature works and what publishers and SEOs should consider.

What Triggers AI Overviews

AI Overviews shows when the user intent is to quickly understand information, especially when that information need is tied to a task.

“AI Overviews appear in Google Search results when our systems determine …when you want to quickly understand information from a range of sources, including information from across the web and Google’s Knowledge Graph.”

In another part of the documentation it ties the trigger to task-based information needs:

“…and use the information they find to advance their tasks.” “

What Kinds Of Sites Does AI Overviews Link To?

An important fact to consider is that just because AI Overviews is triggered by a user’s need to quickly understand something doesn’t mean that only queries with an informational need will trigger the new search feature. Google’s documentation makes it clear that the kinds of websites that will benefit from AI Overviews links includes “creators” (which implies video creators), ecommerce stores and other businesses. This means that far more than informational websites that will benefit from AI overviews.

The new documentation lists the kinds of sites that can receive a link from the AI overviews:

“This allows people to dig deeper and discover a diverse range of content from publishers, creators, retailers, businesses, and more, and use the information they find to advance their tasks.”

Where AI Overviews Sources Information

AI Overviews shows information from the web and the knowledge graph. Large Language Models currently need to be entirely retrained from the ground up when adding significant amounts of new data. That means that the websites chosen to be displayed in Overviews feature are selected from Google’s standard search index which in turn means that Google may be using Retrieval-augmented generation (RAG).

RAG is a system that sits between a large language model and a database of information that’s external to the LLM. This external database can be a specific knowledge like the entire content of an organization’s HR policies to a search index. It’s a supplemental source of information that can be used to double-check the information provided by an LLM or to show where to read more about the question being answered.

The section quoted at the beginning of the article notes that AI Overviews cites sources from the web and the Knowledge Graph:

“AI Overviews appear in Google Search results when our systems determine …when you want to quickly understand information from a range of sources, including information from across the web and Google’s Knowledge Graph.”

What Automatic Inclusion Means For SEO

Inclusion in AI Overviews is automatic and there’s nothing specific to AI Overviews that publishers or SEOs need to do. Google’s documentation says that following their guidelines for ranking in the regular search is all you have to do for ranking in AI Overviews. Google’s “systems” determine what sites are picked to show up for the topics surfaced in AI Overviews.

All the statements seem to confirm that the new Overviews feature sources data from the regular Search Index. It’s possible that Google filters the search index specially for AI Overviews but offhand I can’t think of any reason Google would do that.

All the statements that indicate automatic inclusions point to the likely possibility that Google uses the regular search index:

“No action is needed for publishers to benefit from AI Overviews.”

“AI Overviews show links to resources that support the information in the snapshot, and explore the topic further.”

“…diverse range of content from publishers, creators, retailers, businesses, and more…”

“To rank in AI Overviews, publishers only need to follow the Google Search Essentials guide.

“Google’s systems automatically determine which links appear. There is nothing special for creators to do to be considered other than to follow our regular guidance for appearing in search, as covered in Google Search Essentials.”

Think In Terms Of Topics

Obviously, keywords and synonyms in queries and documents play a role. But in my opinion they play and oversized role in SEO. There are many ways that a search engine can annotate a document in order to match a webpage to a topic, like what Googler Martin Splitt referred to as a centerpiece annotation. A centerpiece annotation is used by Google to label a webpage with what that webpage is about.

Semantic Annotation

This kind of annotation links webpage content to concepts which in turn gives structure to a unstructured document. Every webpage is unstructured data so search engines have to make sense of that. Semantic Annotation is one way to do that.

Google has been matching webpages to concepts since at least 2015. A Google webpage about their cloud products talks about how they integrated neural matching into their Search Engine for the purpose of annotating webpage content with their inherent topics.

This is what Google says about how it matches webpages to concepts:

“Google Search started incorporating semantic search in 2015, with the introduction of noteworthy AI search innovations like deep learning ranking system RankBrain. This innovation was quickly followed with neural matching to improve the accuracy of document retrieval in Search. Neural matching allows a retrieval engine to learn the relationships between a query’s intentions and highly relevant documents, allowing Search to recognize the context of a query instead of the simple similarity search.

Neural matching helps us understand fuzzier representations of concepts in queries and pages, and match them to one another. It looks at an entire query or page rather than just keywords, developing a better understanding of the underlying concepts represented in them.”

Google’s been doing this, matching webpages to concepts, for almost ten years. Google’s documentation about AI Overviews also mentions that showing links to webpages based on topics is a part of determining what sites are ranked in AI Overviews.

Here’s how Google explains it:

“AI Overviews show links to resources that support the information in the snapshot, and explore the topic further.

…AI Overviews offer a preview of a topic or query based on a variety of sources, including web sources.”

Google’s focus on topics has been a thing for a long time and it’s well past time SEOs lessened their grip on keyword targeting and start to also give Topic Targeting a chance to enrich their ability to surface content in Google Search, including in AI Overviews.

Google says that the same optimizations described in their Search Essentials documentation for ranking in Google Search are the same optimizations to apply to rank in Google Overview.

This is exactly what the new documentation says:

“There is nothing special for creators to do to be considered other than to follow our regular guidance for appearing in search, as covered in Google Search Essentials.”

Read Google’s New SEO Related Documentation On AI Overviews

AI Overviews and your website

Featured Image by Shutterstock/Piotr Swat

seo enhancements
Are Google’s new AI Overviews the future of search?

Artificial intelligence (AI) continues to reshape how we interact with information. Google introduced AI Overviews in its search engine yesterday, marking a new milestone. These new features promise to enhance user experience by providing quick, comprehensive answers to complex queries. However, they also raise important questions about the future of content creation, monetization, and information diversity. Where is all of this heading?

Overview of Google’s new AI features

There were many AI announcements at Google I/O yesterday, but we think the AI Overviews will most impact our audience and customers.

AI Overviews are designed to directly summarize search results from multiple sources on the search page. By leveraging advanced generative AI, these overviews provide users with a concise and comprehensive understanding of complex queries without the need to click through multiple websites. AI Overviews won’t appear for every search, only the complex ones.

Benefits of AI Overviews

According to Google, AI Overviews significantly improve users’ efficiency and satisfaction by delivering quick, accurate, and contextually relevant answers. This can be particularly beneficial for users needing immediate information or conducting broad research.

Google understands everything contextually. The AI’s ability to understand and respond to complex, multistep queries ensures that users receive detailed and logically structured answers, which is invaluable for research and education.

AI Overviews provide students, researchers, and professionals with a structured way to access and digest information, making the learning and research process more streamlined and effective.

Challenges and concerns

One of the most significant concerns is the potential reduction in traffic to original content sites. As users find their answers directly on Google, fewer clicks are directed toward independent publishers. This negatively impacts their revenue and overall visibility.

There are also concerns about proper credit and compensation for content creators. If AI Overviews aggregate information without adequately attributing sources, it could disincentivize content creation and harm publishers in the broadest sense.

The shift towards providing answers directly on the search results page may also pose monetization challenges for Google. Less traffic directed to third-party sites could disrupt the traditional ad-based revenue model, but Google has such a stronghold on the ad market it must surely have found a way forward.

Broader implications for our ecosystem

Centralizing information within Google’s ecosystem could reduce content diversity and the number of voices available online. This monopolization of information flow is a significant concern for the web.

As Google becomes more adept at providing comprehensive answers, user reliance on its services will likely increase. This growing dependence could stifle competition and innovation.

Then, there are the privacy and data concerns plaguing AI in general. The extensive data collection required for personalized AI features raises important privacy issues. Ensuring user consent and data security will be paramount as AI evolves.

Google wants to become your Star Trek Communicator

Google’s advancements in AI are steering it towards becoming a highly personalized digital assistant, akin to the Communicator from Star Trek. It has hinted at this many times over the past. Still, with yesterday’s news, that vision is becoming clearer. This vision involves creating a seamless, always-available assistant to understand and respond to complex human queries and tasks in real time.

Google aims to offer a seamless user experience by integrating multimodal AI capabilities across its ecosystem. This includes personalized interactions that understand user preferences and provide tailored recommendations and insights.

Looking ahead, Google is likely to further integrate AI into various aspects of daily life, from home automation to personal finance and health, making it an indispensable part of users’ routines.

Balancing innovation with responsibility

Google doesn’t owe us traffic or high rankings, but the future of the web does hang in the balance of whatever Google thinks of next. The discussions about who feeds all those AI machines continue. As a site owner, quitting publishing content is not an easy decision. However, seeing the ROI of these publications slowly die might make that decision easier.

We think it is too early to write off search yet. There are plenty of opportunities to be had, and Google might devise a way to balance providing enough value for site owners vs. ad revenue. To address the impact on content creators, Google could find ways to ensure fair compensation for using their content in AI Overviews.

Ethical development of AI

Google’s AI Overviews will occasionally be flat-out wrong — as we’ve seen many times over the past year. Hallucinations are still happening, and they could put Google in a jam. Therefore, it must prioritize ethical considerations in AI development, including transparency, fairness, and user consent. This will help build trust and ensure that AI advancements benefit everyone.

To create a sustainable digital ecosystem for everyone, we need a collaborative approach involving Google, publishers, regulators, and users. Open dialogue and cooperation can help address the challenges and harness AI’s full potential.

Conclusion to Google’s AI Overviews

The introduction of AI Overviews in Google search represents a significant advancement. It has the potential to transform how we access information.

While the benefits for users are clear, the challenges for independent publishers and our ecosystem cannot be ignored. Ultimately, Google hopes to create a future where AI enhances our lives while supporting a diverse and thriving web.

Now that AI-driven search is here, Google wants you to “Let Google do the googling for you,” but we hope it has carefully considered the broader impacts on our ecosystem. Ultimately, we hope everyone benefits from these advancements — not just Google.

Coming up next!

Competing Against Brands & Nouns Of The Same Name via @sejournal, @TaylorDanRW

Establishing and building a brand has always been both a challenge and an investment, even before the days of the internet.

One thing the internet has done, however, is make the world a lot smaller, and the frequency of brand (or noun) conflicts has greatly increased.

In the past year, I’ve been emailed and asked questions about these conflicts at conferences more than I have in my entire SEO career.

When you share your brand name with another brand, town, or city, Google has to decide and determine the dominant user interpretation of the query – or at least, if there are multiple common interpretations, the most common interpretations.

Noun and brand conflicts typically happen when:

  • A rebrand’s research focuses on other business names and doesn’t take into consideration general user search.
  • When a brand chooses a word in one language, but it has a use in another.
  • A name is chosen that is also a noun (e.g. the name of a town or city).

Some examples include Finlandia, which is both a brand of cheese and vodka; Graco, which is both a brand of commercial products and a brand of baby products; and Kong, which is both the name of a pet toy manufacturer and a tech company.

User Interpretations

From conversations I’ve had with marketers and SEO pros working for various brands with this issue, the underlying theme (and potential cause) comes down to how Google handles interpretation of what users are looking for.

When a user enters a query, Google processes the query to identify known entities that are contained.

It does this to improve the relevance of search results being returned (as outlined in its 2015 Patent #9,009,192). From this, Google also works to return related, relevant results and search engine results page (SERP) elements.

For example, when you search for a specific film or TV series, Google may return a SERP feature containing relevant actors or news (if deemed relevant) about the media.

This then leads to interpretation.

When Google receives a query, the search results need to often cater for multiple common interpretations and intents. This is no different when someone searches for a recognized branded entity like Nike.

When I search for Nike, I get a search results page that is a combination of branded web assets such as the Nike website and social media profiles, the Map Pack showing local stores, PLAs, the Nike Knowledge Panel, and third-party online retailers.

This variation is to cater for the multiple interpretations and intents that a user just searching for “Nike” may have.

Brand Entity Disambiguation

Now, if we look at brands that share a name such as Kong, when Google checks for entities and references against the Knowledge Graph (and knowledge base sources), it gets two closer matches: Kong Company and Kong, Inc.

The search results page is also littered with product listing ads (PLAs) and ecommerce results for pet toys, but the second blue link organic result is Kong, Inc.

Also on page one, we can find references to a restaurant with the same name (UK-based search), and in the image carousel, Google is introducing the (King) Kong film franchise.

It is clear that Google sees the dominant interpretation of this query to be the pet toy company, but has diversified the SERP further to cater for secondary and tertiary meanings.

In 2015, Google was granted a patent that included features of how Google might determine differences in entities of the same name.

This includes the possible use of annotations within the Knowledge Base – such as the addition of a word or descriptor – to help disambiguate entities with the same name. For example, the entries for Dan Taylor could be:

  • Dan Taylor (marketer).
  • Dan Taylor (journalist).
  • Dan Taylor (olympian).

How it determines what is the “dominant” interpretation of the query, and then how to order search results and the types of results, from experience, comes down to:

  • Which results users are clicking on when they perform the query (SERP interaction).
  • How established the entity is within the user’s market/region.
  • How closely the entity is related to previous queries the user has searched (personalization).

I’ve also observed that there is a correlation between extended brand searches and how they affect exact match branded search.

It’s also worth highlighting that this can be dynamic. Should a brand start receiving a high volume of mentions from multiple news publishers, Google will take this into account and amend the search results to better meet users’ needs and potential query interpretations at that moment in time.

SEO For Brand Disambiguation

Building a brand is not a task solely on the shoulders of SEO professionals. It requires buy-in from the wider business and ensuring the brand and brand messaging are both defined and aligned.

SEO can, however, influence this effort through the full spectrum of SEO: technical, content, and digital PR.

Google understands entities on the concept of relatedness, and this is determined by the co-occurrence of entities and then how Google classifies and discriminates between those entities.

We can influence this through technical SEO through granular Schema markup and by making sure the brand name is consistent across all web properties and references.

This ties into how we then write about the brand in our content and the co-occurrence of the brand name with other entity types.

To reinforce this and build brand awareness, this should be coupled with digital PR efforts with the objective of brand placement and corroborating topical relevance.

A Note On Search Generative Experience

As it looks likely that Search Generative Experience is going to be the future of search, or at least components of it, it’s worth noting that in tests we’ve done, Google can, at times, have issues when generative AI snapshots for brands, when there are multiple brands with the same name.

To check your brand’s exposure, I recommend asking Google and generating an SGE snapshot for your brand + reviews.

If Google isn’t 100% sure which brand you mean, it will start to include reviews and comments on companies of the same (or very similar) name.

It does disclose that they are different companies in the snapshot, but if your user is skim-reading and only looking at the summaries, this could be an accidental negative brand touchpoint.

More resources:


Featured Image: VectorMine/Shutterstock

Google’s Astra is its first AI-for-everything agent

Google is set to introduce a new system called Astra later this year and promises that it will be the most powerful, advanced type of AI assistant it’s ever launched. 

The current generation of AI assistants, such as ChatGPT, can retrieve information and offer answers, but that is about it. But this year, Google is rebranding its assistants as more advanced “agents,” which it says could  show reasoning, planning, and memory skills and are able to take multiple steps to execute tasks. 

People will be able to use Astra through their smartphones and possibly desktop computers, but the company is exploring other options too, such as embedding it into smart glasses or other devices, Oriol Vinyals, vice president of research at Google DeepMind, told MIT Technology Review

“We are in very early days [of AI agent development],” Google CEO Sundar Pichai said on a call ahead of Google’s I/O conference today. 

“We’ve always wanted to build a universal agent that will be useful in everyday life,” said Demis Hassabis, the CEO and cofounder of Google DeepMind. “Imagine agents that can see and hear what we do, better understand the context we’re in, and respond quickly in conversation, making the pace and quality of interaction feel much more natural.” That, he says, is what Astra will be. 

Google’s announcement comes a day after competitor OpenAI unveiled its own supercharged AI assistant, GPT-4o. Google DeepMind’s Astra responds to audio and video inputs, much in the same way as GPT-4o (albeit it less flirtatiously). 

In a press demo, a user pointed a smartphone camera and smart glasses at things and asked Astra to explain what they were. When the person pointed the device out the window and asked “What neighborhood do you think I’m in?” the AI system was able to identify King’s Cross, London, site of Google DeepMind’s headquarters. It was also able to say that the person’s glasses were on a desk, having recorded them earlier in the interaction. 

The demo showcases Google DeepMind’s vision of multimodal AI (which can handle multiple types of input—voice, video, text, and so on) working in real time, Vinyals says. 

“We are very excited about, in the future, to be able to really just get closer to the user, assist the user with anything that they want,” he says. Google recently upgraded its artificial-intelligence model Gemini to process even larger amounts of data, an upgrade which helps it handle bigger documents and videos, and have longer conversations. 

Tech companies are in the middle of a fierce competition over AI supremacy, and  AI agents are the latest effort from Big Tech firms to show they are pushing the frontier of development. Agents also play into a narrative by many tech companies, including OpenAI and Google DeepMind, that aim to build artificial general intelligence, a highly hypothetical idea of superintelligent AI systems. 

“Eventually, you’ll have this one agent that really knows you well, can do lots of things for you, and can work across multiple tasks and domains,” says Chirag Shah, a professor at the University of Washington who specializes in online search.

This vision is still aspirational. But today’s announcement should be seen as Google’s attempt to keep up with competitors. And by rushing these products out, Google can collect even more data from its over a billion users on how they are using their models and what works, Shah says.

Google is unveiling many more new AI capabilities beyond agents today. It’s going to integrate AI more deeply into Search through a new feature called AI overviews, which gather information from the internet and package them into short summaries in response to search queries. The feature, which launches today, will initially be available only in the US, with more countries to gain access later. 

This will help speed up the search process and get users more specific answers to more complex, niche questions, says Felix Simon, a research fellow in AI and digital news at the Reuters Institute for Journalism. “I think that’s where Search has always struggled,” he says. 

Another new feature of Google’s AI Search offering is better planning. People will soon be able to ask Search to make meal and travel suggestions, for example, much like asking a travel agent to suggest restaurants and hotels. Gemini will be able to help them plan what they need to do or buy to cook recipes, and they will also be able to have conversations with the AI system, asking it to do anything from relatively mundane tasks, such as informing them about the weather forecast, to highly complex ones like helping them prepare for a job interview or an important speech. 

People will also be able to interrupt Gemini midsentence and ask clarifying questions, much as in a real conversation. 

In another move to one-up competitor OpenAI, Google also unveiled Veo, a new video-generating AI system. Veo is able to generate short videos and allows users more control over cinematic styles by understanding prompts like “time lapse” or “aerial shots of a landscape.”

Google has a significant advantage when it comes to training generative video models, because it owns YouTube. It’s already announced collaborations with artists such as Donald Glover and Wycleaf Jean, who are using its technology to produce their work. 

Earlier this year, OpenA’s CTO, Mira Murati, fumbled when asked about whether the company’s model was trained on YouTube data. Douglas Eck, senior research director at Google DeepMind, was also vague about the training data used to create Veo when asked about by MIT Technology Review, but he said that it “may be trained on some YouTube content in accordance with our agreements with YouTube creators.”

On one hand, Google is presenting its generative AI as a tool artists can use to make stuff, but the tools likely get their ability to create that stuff by using material from existing artists, says Shah. AI companies such as Google and OpenAI have faced a slew of lawsuits by writers and artists claiming that their intellectual property has been used without consent or compensation.  

“For artists it’s a double-edged sword,” says Shah. 

New Ecommerce Tools: May 14, 2024

Every week we publish a rundown of new products from companies offering services to ecommerce and omnichannel merchants. This installment includes updates on shoppable video, digital payments, returns management, AI-powered pricing, generative AI ad tools, and advertising for marketplace merchants.

Got an ecommerce product release? Email releases@practicalecommerce.com.

New Tools for Merchants: May 14

BigCommerce launches B2B Edition Buyer Portal. BigCommerce has launched the open-source B2B Edition Buyer Portal, now available for single and multi-storefronts with localized buyer experiences, including language, content, pricing, and promotions. From a single backend, merchants can curate tailored purchasing experiences based on a buyer’s specific region, industry vertical, and unique buying processes, as well as integrated servicing experiences specific to sectors, including warranties, customer support, and product servicing.

Home page of BigCommmerce B2B

BigCommerce

Meta launches enhanced generative AI features for advertisers. With Meta’s new AI features, advertisers can create full image variations inspired by original ad creatives with text overlay capabilities and a dozen popular font typeface options. Image expansion is now available on Reels and feeds across Instagram and Facebook. Also, the text generation feature creates variations for the ad headline in addition to the primary text.

Loop integrates returns management software with Salesforce Commerce Cloud. Loop, a returns and reverse logistics platform, now supports merchants on Salesforce Commerce Cloud, expanding its footprint beyond Shopify’s ecosystem. Merchants operating on Salesforce Commerce Cloud will gain access to Loop’s returns management software to promote item exchanges, synchronize order data, automate returns processes, leverage Loop’s analytics to ensure efficiency, and more.

Amazon Ads announces three streaming TV ad formats. Amazon Ads has unveiled an expanded suite of interactive and shoppable ad formats for Prime Video with remote-enabled capabilities for living-room devices. In the upcoming broadcast year, brands can use shoppable carousel ads to help viewers browse and shop multiple product variations on Amazon during ad breaks in shows and movies on Prime Video. Brands can also use interactive pause and trivia ads in Prime Video TV shows, movies, and live sports.

Amazon Ads

Digital payments platform Checkout.com launches Flow to optimize payments. Checkout.com, a global digital payments provider, has launched Flow, utilizing customizable building blocks to help businesses present the right payment methods to customers. Flow enables merchants to enter new markets by easily integrating new payment methods and improving security by staying up-to-date with PCI compliance, GDPR rules, and card scheme requirements. Flow also optimizes input fields to capture payment information in the correct format to reduce cart abandonment.

Google launches Performance Max for Marketplaces. Google Ads has launched Performance Max for Marketplaces to help merchants reach customers and drive product sales on marketplaces without needing a website or Google Merchant Center account. Available only on select marketplaces, Performance Max uses Google’s automation so that products can also be advertised across all Google Ads channels, including Search and Shopping.

Spresso launches BigCommerce app with AI-powered pricing. Spresso, a provider of AI-powered ecommerce tools, has launched its Pricing Intelligence app available through the BigCommerce marketplace. The integration enables BigCommerce merchants to activate Spresso’s pricing intelligence technology. Merchants can integrate AI-powered pricing with just a few clicks.

Home page of Spresso

Spresso

eBay launches “Ask Me About” series. eBay has announced the launch of a series called “Ask Me About,” replacing its “Monthly Chat with eBay Staff” program. According to eBay, the new series is designed to bring sellers closer to eBay’s operations and provide insider insights. The introductory episode will focus on product research, featuring the product research team with responses to questions submitted in the previous week.

Wix launches AI Portfolio Creator. Wix.com, a website builder, has announced the launch of AI Portfolio Creator, which enhances how users create and showcase an online professional portfolio. Users select the type of portfolio they would like to create and upload the desired work to showcase. Once selections are made and images are uploaded, the AI Creator quickly organizes and generates project options with clustered images, suggested titles and descriptions, and customizable layout options.

dotCMS and DSS Partners announce integration with Intershop Commerce Platform. dotCMS, a content management platform, and DSS Partners, a digital consultancy and system integrator, have launched a strategic integration between dotCMS and Intershop’s Commerce Platform. The integration eliminates the need to build a custom CMS integration in Intershop. Users can work within the Intershop platform while leveraging all the capabilities of dotCMS.

CedCommerce launches a free integration tool for European sellers in partnership with AliExpress. CedCommerce, a multichannel enabler, has announced a collaboration with AliExpress. This free integration tool provides a gateway for ecommerce sellers using platforms such as WooCommerce, Prestashop, and Adobe Commerce, enabling them to connect their stores with AliExpress, offer their product catalog, and sync and fulfill the orders from the ecommerce store itself. Using CedCommerce, sellers can maintain centralized control over inventory levels, list products in bulk in a single click, and simplify order processing and fulfillment.

Home page of CedCommerce

CedCommerce

AI Tools to Convert Articles into Videos

Generative AI has made video creation available to businesses with little time or money, facilitating marketing on platforms such as YouTube, TikTok, and Instagram. I first addressed text-to-video tools last fall.

Here are three more tools that can quickly turn an article into a video.

Brainy Docs

Home page of Brainy Docs

Brainy Docs

Brainy Docs turns PDFs into summaries, presentations, and explainer-type videos. For my test, I downloaded my article last week, “Using ChatGPT’s Memory Feature,” as a PDF from Google Docs and uploaded it to the site. The tool:

  • Created a script,
  • Produced an audio narration from the script,
  • Assembled a video with takeaways,
  • Generated a PowerPoint presentation with screenshots.

Brainy Docs converts one monthly 20-page PDF to a video for free. Paid plans start at $9.99 per month for three PDFs totaling 60 pages.

CopyCopter

Home page of CopyCopter

CopyCopter

CopyCopter generates videos from text. Simply paste a document, select a voice, and pick an image type (stock photography or AI-generated). CopyCopter will create a script and video.

Then use the built-in video editor to replace images, customize the captions, or change the voice.

My test took just a few minutes, from pasting the text to receiving the video. CopyCopter’s automatically selected images were relevant and contextual. I edited very little. It’s a handy way to promote an article on video-driven social media.

CopyCopter creates one video for free with registered accounts. Paid plans start at $19 per month for five videos.

DeepBrain

Home page of DeepBrain

DeeBrain

DeepBrain creates videos featuring AI avatars. Select one avatar to narrate or two to dialogue in an interview style.

Paste your text and select an avatar(s) and voice(s). DeepBrain will create an editable script and images. Add captions and titles, and upload your own images as desired.

DeepBrain creates a 60-second video with a watermark for free. Paid plans start at $29 per month for five minutes of videos and no watermark.

Google Rolls Out New ‘Web’ Filter For Search Results via @sejournal, @MattGSouthern

Google is introducing a filter that allows you to view only text-based webpages in search results.

The “Web” filter, rolling out globally over the next two days, addresses demand from searchers who prefer a stripped-down, simplified view of search results.

Danny Sullivan, Google’s Search Liaison, states in an announcement:

“We’ve added this after hearing from some that there are times when they’d prefer to just see links to web pages in their search results, such as if they’re looking for longer-form text documents, using a device with limited internet access, or those who just prefer text-based results shown separately from search features.”

The new functionality is a throwback to when search results were more straightforward. Now, they often combine rich media like images, videos, and shopping ads alongside the traditional list of web links.

How It Works

On mobile devices, the “Web” filter will be displayed alongside other filter options like “Images” and “News.”

Screenshot from: twitter.com/GoogleSearchLiaison, May 2024.

If Google’s systems don’t automatically surface it based on the search query, desktop users may need to select “More” to access it.

Screenshot from: twitter.com/GoogleSearchLiaison, May 2024.

More About Google Search Filters

Google’s search filters allow you to narrow results by type. The options displayed are dynamically generated based on your search query and what Google’s systems determine could be most relevant.

The “All Filters” option provides access to filters that are not shown automatically.

Alongside filters, Google also displays “Topics” – suggested related terms that can further refine or expand a user’s original query into new areas of exploration.

For more about Google’s search filters, see its official help page.


Featured Image: egaranugrah/Shutterstock

Was OpenAI GPT-4o Hype A Troll On Google? via @sejournal, @martinibuster

OpenAI managed to steal the attention away from Google in the weeks leading up to Google’s biggest event of the year (Google I/O). When the big announcement arrived there all they had to show was a language model that was slightly better than the previous one with the “magic” part not even in Alpha testing stage.

OpenAI may have left users feeling like a mom receiving a vacuum cleaner for Mothers Day but it surely succeeded in minimizing press attention for Google’s important event.

The Letter O

The first hint that there’s at least a little trolling going on is the name of the new GPT model, 4 “o” with the letter “o” as in the name of Google’s event,  I/O.

OpenAI says that the letter O stands for Omni, which means everything, but it sure seems like there’s a subtext to that choice.

GPT-4o Oversold As Magic

Sam Altman in a tweet the Friday before the announcement promised “new stuff” that felt like “magic” to him:

“not gpt-5, not a search engine, but we’ve been hard at work on some new stuff we think people will love! feels like magic to me.”

OpenAI co-founder Greg Brockman tweeted:

“Introducing GPT-4o, our new model which can reason across text, audio, and video in real time.

It’s extremely versatile, fun to play with, and is a step towards a much more natural form of human-computer interaction (and even human-computer-computer interaction):”

The announcement itself explained that previous versions of ChatGPT used three models to process audio input. One model to turn audio input into text. Another model to complete the task and output the text version of it and a third model to turn the text output into audio. The breakthrough for GPT-4o is that it can now process the audio input and output within a single model and output it all in the same amount of time that it takes a human to listen and respond to a question.

But the problem is that the audio part isn’t online yet. They’re still working on getting the guardrails working and it will take weeks before an Alpha version is released to a few users for testing. Alpha versions are expected to possibly have bugs while the Beta versions are generally closer to the final products.

This is how OpenAI explained the disappointing delay:

“We recognize that GPT-4o’s audio modalities present a variety of novel risks. Today we are publicly releasing text and image inputs and text outputs. Over the upcoming weeks and months, we’ll be working on the technical infrastructure, usability via post-training, and safety necessary to release the other modalities.

The most important part of GPT-4o, the audio input and output, is finished but the safety level is not yet ready for public release.

Some Users Disappointed

It’s inevitable that an incomplete and oversold product would generate some negative sentiment on social media.

AI engineer Maziyar Panahi (LinkedIn profile) tweeted his disappointment:

“I’ve been testing the new GPT-4o (Omni) in ChatGPT. I am not impressed! Not even a little! Faster, cheaper, multimodal, these are not for me.
Code interpreter, that’s all I care and it’s as lazy as it was before!”

He followed up with:

“I understand for startups and businesses the cheaper, faster, audio, etc. are very attractive. But I only use the Chat, and in there it feels pretty much the same. At least for Data Analytics assistant.

Also, I don’t believe I get anything more for my $20. Not today!”

There are others across Facebook and X that expressed similar sentiments although many others were happy with what they felt was an improvement in speed and cost for the API usage.

Did OpenAI Oversell GPT-4o?

Given that the GPT-4o is in an unfinished state it’s hard not to miss the impression that the release was timed to coincide with and detract from Google I/O. Releasing it on the eve of Google’s big day with a half-finished product may have inadvertently created the impression that GPT-4o in the current state is a minor iterative improvement.

In the current state it’s not a revolutionary step forward but once the audio portion of the model exits Alpha testing stage and makes it through the Beta testing stage then we can start talking about revolutions in large language model. But by the time that happens Google and Anthropic may already have staked a flag on that mountain.

OpenAI’s announcement paints a lackluster image of the new model, promoting the performance as on the same level as GPT-4 Turbo. The only bright spots is the significant improvements in languages other than English and for API users.

OpenAI explains:

  • “It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50% cheaper in the API.”

Here are the ratings across six benchmarks that shows GPT-4o barely squeaking past GPT-4T in most tests but falling behind GPT-4T in an important benchmark for reading comprehension.

Here are the scores:

  • MMLU (Massive Multitask Language Understanding)
    This is a benchmark for multitasking accuracy and problem solving in over fifty topics like math, science, history and law. GPT-4o (scoring 88.7) is slightly ahead of GPT4 Turbo (86.9).
  • GPQA (Graduate-Level Google-Proof Q&A Benchmark)
    This is 448 multiple-choice questions written by human experts in various fields like biology, chemistry, and physics. GPT-4o scored 53.6, slightly outscoring GPT-4T (48.0).
  • Math
    GPT 4o (76.6) outscores GPT-4T by four points (72.6).
  • HumanEval
    This is the coding benchmark. GPT-4o (90.2) slightly outperforms GPT-4T (87.1) by about three points.
  • MGSM (Multilingual Grade School Math Benchmark)
    This tests LLM grade-school level math skills across ten different languages. GPT-4o scores 90.5 versus 88.5 for GPT-4T.
  • DROP (Discrete Reasoning Over Paragraphs)
    This is a benchmark comprised of 96k questions that tests language model comprehension over the contents of paragraphs. GPT-4o (83.4) scores nearly three points lower than GPT-4T (86.0).

Did OpenAI Troll Google With GPT-4o?

Given the provocatively named model with the letter o, it’s hard to not consider that OpenAI is trying to steal media attention in the lead-up to Google’s important I/O conference. Whether that was the intention or not OpenAI wildly succeeded in minimizing attention given to Google’s upcoming search conference.

Does a language model that barely outperforms its predecessor worth all the hype and media attention it received? The pending announcement dominated news coverage over Google’s big event so for OpenAI the answer is clearly yes, it was worth the hype.

Featured Image by Shutterstock/BeataGFX