Tag Archives: chatbot

Chatbot with expertise in nanomaterials

This December 1, 2023 news item on phys.org starts with a story,

A researcher has just finished writing a scientific paper. She knows her work could benefit from another perspective. Did she overlook something? Or perhaps there’s an application of her research she hadn’t thought of. A second set of eyes would be great, but even the friendliest of collaborators might not be able to spare the time to read all the required background publications to catch up.

Kevin Yager—leader of the electronic nanomaterials group at the Center for Functional Nanomaterials (CFN), a U.S. Department of Energy (DOE) Office of Science User Facility at DOE’s Brookhaven National Laboratory—has imagined how recent advances in artificial intelligence (AI) and machine learning (ML) could aid scientific brainstorming and ideation. To accomplish this, he has developed a chatbot with knowledge in the kinds of science he’s been engaged in.

A December 1, 2023 DOE/Brookhaven National Laboratory news release by Denise Yazak (also on EurekAlert), which originated the news item, describes a research project with a chatbot that has nanomaterial-specific knowledge, Note: Links have been removed,

Rapid advances in AI and ML have given way to programs that can generate creative text and useful software code. These general-purpose chatbots have recently captured the public imagination. Existing chatbots—based on large, diverse language models—lack detailed knowledge of scientific sub-domains. By leveraging a document-retrieval method, Yager’s bot is knowledgeable in areas of nanomaterial science that other bots are not. The details of this project and how other scientists can leverage this AI colleague for their own work have recently been published in Digital Discovery.

Rise of the Robots

“CFN has been looking into new ways to leverage AI/ML to accelerate nanomaterial discovery for a long time. Currently, it’s helping us quickly identify, catalog, and choose samples, automate experiments, control equipment, and discover new materials. Esther Tsai, a scientist in the electronic nanomaterials group at CFN, is developing an AI companion to help speed up materials research experiments at the National Synchrotron Light Source II (NSLS-II).” NSLS-II is another DOE Office of Science User Facility at Brookhaven Lab.

At CFN, there has been a lot of work on AI/ML that can help drive experiments through the use of automation, controls, robotics, and analysis, but having a program that was adept with scientific text was something that researchers hadn’t explored as deeply. Being able to quickly document, understand, and convey information about an experiment can help in a number of ways—from breaking down language barriers to saving time by summarizing larger pieces of work.

Watching Your Language

To build a specialized chatbot, the program required domain-specific text—language taken from areas the bot is intended to focus on. In this case, the text is scientific publications. Domain-specific text helps the AI model understand new terminology and definitions and introduces it to frontier scientific concepts. Most importantly, this curated set of documents enables the AI model to ground its reasoning using trusted facts.

To emulate natural human language, AI models are trained on existing text, enabling them to learn the structure of language, memorize various facts, and develop a primitive sort of reasoning. Rather than laboriously retrain the AI model on nanoscience text, Yager gave it the ability to look up relevant information in a curated set of publications. Providing it with a library of relevant data was only half of the battle. To use this text accurately and effectively, the bot would need a way to decipher the correct context.

“A challenge that’s common with language models is that sometimes they ‘hallucinate’ plausible sounding but untrue things,” explained Yager. “This has been a core issue to resolve for a chatbot used in research as opposed to one doing something like writing poetry. We don’t want it to fabricate facts or citations. This needed to be addressed. The solution for this was something we call ‘embedding,’ a way of categorizing and linking information quickly behind the scenes.”

Embedding is a process that transforms words and phrases into numerical values. The resulting “embedding vector” quantifies the meaning of the text. When a user asks the chatbot a question, it’s also sent to the ML embedding model to calculate its vector value. This vector is used to search through a pre-computed database of text chunks from scientific papers that were similarly embedded. The bot then uses text snippets it finds that are semantically related to the question to get a more complete understanding of the context.

The user’s query and the text snippets are combined into a “prompt” that is sent to a large language model, an expansive program that creates text modeled on natural human language, that generates the final response. The embedding ensures that the text being pulled is relevant in the context of the user’s question. By providing text chunks from the body of trusted documents, the chatbot generates answers that are factual and sourced.

“The program needs to be like a reference librarian,” said Yager. “It needs to heavily rely on the documents to provide sourced answers. It needs to be able to accurately interpret what people are asking and be able to effectively piece together the context of those questions to retrieve the most relevant information. While the responses may not be perfect yet, it’s already able to answer challenging questions and trigger some interesting thoughts while planning new projects and research.”

Bots Empowering Humans

CFN is developing AI/ML systems as tools that can liberate human researchers to work on more challenging and interesting problems and to get more out of their limited time while computers automate repetitive tasks in the background. There are still many unknowns about this new way of working, but these questions are the start of important discussions scientists are having right now to ensure AI/ML use is safe and ethical.

“There are a number of tasks that a domain-specific chatbot like this could clear from a scientist’s workload. Classifying and organizing documents, summarizing publications, pointing out relevant info, and getting up to speed in a new topical area are just a few potential applications,” remarked Yager. “I’m excited to see where all of this will go, though. We never could have imagined where we are now three years ago, and I’m looking forward to where we’ll be three years from now.”

For researchers interested in trying this software out for themselves, the source code for CFN’s chatbot and associated tools can be found in this github repository.

Brookhaven National Laboratory is supported by the Office of Science of the U.S. Department of Energy. The Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, visit science.energy.gov.

Here’s a link to and a citation for the paper,

Domain-specific chatbots for science using embeddings by Kevin G. Yager.
Digital Discovery, 2023,2, 1850-1861 DOI: https://doi.org/10.1039/D3DD00112A
First published 10 Oct 2023

This paper appears to be open access.

Should AI algorithms get patents for their inventions and is anyone talking about copyright for texts written by AI algorithms?

A couple of Australian academics have written a comment for the journal Nature, which bears the intriguing subtitle: “The patent system assumes that inventors are human. Inventions devised by machines require their own intellectual property law and an international treaty.” (For the curious, I’ve linked to a few of my previous posts touching on intellectual property [IP], specifically the patent’s fraternal twin, copyright at the end of this piece.)

Before linking to the comment, here’s the May 27, 2022 University of New South Wales (UNCSW) press release (also on EurekAlert but published May 30, 2022) which provides an overview of their thinking on the subject, Note: Links have been removed,

It’s not surprising these days to see new inventions that either incorporate or have benefitted from artificial intelligence (AI) in some way, but what about inventions dreamt up by AI – do we award a patent to a machine?

This is the quandary facing lawmakers around the world with a live test case in the works that its supporters say is the first true example of an AI system named as the sole inventor.

In commentary published in the journal Nature, two leading academics from UNSW Sydney examine the implications of patents being awarded to an AI entity.

Intellectual Property (IP) law specialist Associate Professor Alexandra George and AI expert, Laureate Fellow and Scientia Professor Toby Walsh argue that patent law as it stands is inadequate to deal with such cases and requires legislators to amend laws around IP and patents – laws that have been operating under the same assumptions for hundreds of years.

The case in question revolves around a machine called DABUS (Device for the Autonomous Bootstrapping of Unified Sentience) created by Dr Stephen Thaler, who is president and chief executive of US-based AI firm Imagination Engines. Dr Thaler has named DABUS as the inventor of two products – a food container with a fractal surface that helps with insulation and stacking, and a flashing light for attracting attention in emergencies.

For a short time in Australia, DABUS looked like it might be recognised as the inventor because, in late July 2021, a trial judge accepted Dr Thaler’s appeal against IP Australia’s rejection of the patent application five months earlier. But after the Commissioner of Patents appealed the decision to the Full Court of the Federal Court of Australia, the five-judge panel upheld the appeal, agreeing with the Commissioner that an AI system couldn’t be named the inventor.

A/Prof. George says the attempt to have DABUS awarded a patent for the two inventions instantly creates challenges for existing laws which has only ever considered humans or entities comprised of humans as inventors and patent-holders.

“Even if we do accept that an AI system is the true inventor, the first big problem is ownership. How do you work out who the owner is? An owner needs to be a legal person, and an AI is not recognised as a legal person,” she says.

Ownership is crucial to IP law. Without it there would be little incentive for others to invest in the new inventions to make them a reality.

“Another problem with ownership when it comes to AI-conceived inventions, is even if you could transfer ownership from the AI inventor to a person: is it the original software writer of the AI? Is it a person who has bought the AI and trained it for their own purposes? Or is it the people whose copyrighted material has been fed into the AI to give it all that information?” asks A/Prof. George.

For obvious reasons

Prof. Walsh says what makes AI systems so different to humans is their capacity to learn and store so much more information than an expert ever could. One of the requirements of inventions and patents is that the product or idea is novel, not obvious and is useful.

“There are certain assumptions built into the law that an invention should not be obvious to a knowledgeable person in the field,” Prof. Walsh says.

“Well, what might be obvious to an AI won’t be obvious to a human because AI might have ingested all the human knowledge on this topic, way more than a human could, so the nature of what is obvious changes.”

Prof. Walsh says this isn’t the first time that AI has been instrumental in coming up with new inventions. In the area of drug development, a new antibiotic was created in 2019 – Halicin – that used deep learning to find a chemical compound that was effective against drug-resistant strains of bacteria.

“Halicin was originally meant to treat diabetes, but its effectiveness as an antibiotic was only discovered by AI that was directed to examine a vast catalogue of drugs that could be repurposed as antibiotics. So there’s a mixture of human and machine coming into this discovery.”

Prof. Walsh says in the case of DABUS, it’s not entirely clear whether the system is truly responsible for the inventions.

“There’s lots of involvement of Dr Thaler in these inventions, first in setting up the problem, then guiding the search for the solution to the problem, and then interpreting the result,” Prof. Walsh says.

“But it’s certainly the case that without the system, you wouldn’t have come up with the inventions.”

Change the laws

Either way, both authors argue that governing bodies around the world will need to modernise the legal structures that determine whether or not AI systems can be awarded IP protection. They recommend the introduction of a new ‘sui generis’ form of IP law – which they’ve dubbed ‘AI-IP’ – that would be specifically tailored to the circumstances of AI-generated inventiveness. This, they argue, would be more effective than trying to retrofit and shoehorn AI-inventiveness into existing patent laws.

Looking forward, after examining the legal questions around AI and patent law, the authors are currently working on answering the technical question of how AI is going to be inventing in the future.

Dr Thaler has sought ‘special leave to appeal’ the case concerning DABUS to the High Court of Australia. It remains to be seen whether the High Court will agree to hear it. Meanwhile, the case continues to be fought in multiple other jurisdictions around the world.

Here’s a link to and a citation for the paper,

Artificial intelligence is breaking patent law by Alexandra George & Toby Walsh. Nature (Nature) COMMENT ISSN 1476-4687 (online) 24 May 2022 ISSN 0028-0836 (print) Vol 605 26 May 2022 pp. 616-18 DOI: 10.1038/d41586-022-01391-x

This paper appears to be open access.

The Journey

DABIUS has gotten a patent in one jurisdiction, from an August 8, 2021 article on brandedequity.com,

The patent application listing DABUS as the inventor was filed in patent offices around the world, including the US, Europe, Australia, and South Afica. But only South Africa granted the patent (Australia followed suit a few days later after a court judgment gave the go-ahard [and rejected it several months later]).

Natural person?

This September 27, 2021 article by Miguel Bibe for Inventa covers some of the same ground adding some some discussion of the ‘natural person’ problem,

The patent is for “a food container based on fractal geometry”, and was accepted by the CIPC [Companies and Intellectual Property Commission] on June 24, 2021. The notice of issuance was published in the July 2021 “Patent Journal”.  

South Africa does not have a substantive patent examination system and, instead, requires applicants to merely complete a filing for their inventions. This means that South Africa patent laws do not provide a definition for “inventor” and the office only proceeds with a formal examination in order to confirm if the paperwork was filled correctly.

… according to a press release issued by the University of Surrey: “While patent law in many jurisdictions is very specific in how it defines an inventor, the DABUS team is arguing that the status quo is not fit for purpose in the Fourth Industrial Revolution.”

On the other hand, this may not be considered as a victory for the DABUS team since several doubts and questions remain as to who should be considered the inventor of the patent. Current IP laws in many jurisdictions follow the traditional term of “inventor” as being a “natural person”, and there is no legal precedent in the world for inventions created by a machine.

August 2022 update

Mike Masnick in an August 15, 2022 posting on Techdirt provides the latest information on Stephen Thaler’s efforts to have patents and copyrights awarded to his AI entity, DABUS,

Stephen Thaler is a man on a mission. It’s not a very good mission, but it’s a mission. He created something called DABUS (Device for the Autonomous Bootstrapping of Unified Sentience) and claims that it’s creating things, for which he has tried to file for patents and copyrights around the globe, with his mission being to have DABUS named as the inventor or author. This is dumb for many reasons. The purpose of copyright and patents are to incentivize the creation of these things, by providing to the inventor or author a limited time monopoly, allowing them to, in theory, use that monopoly to make some money, thereby making the entire inventing/authoring process worthwhile. An AI doesn’t need such an incentive. And this is why patents and copyright only are given to persons and not animals or AI.

… Thaler’s somewhat quixotic quest continues to fail. The EU Patent Office rejected his application. The Australian patent office similarly rejected his request. In that case, a court sided with Thaler after he sued the Australian patent office, and said that his AI could be named as an inventor, but thankfully an appeals court set aside that ruling a few months ago. In the US, Thaler/DABUS keeps on losing as well. Last fall, he lost in court as he tried to overturn the USPTO ruling, and then earlier this year, the US Copyright Office also rejected his copyright attempt (something it has done a few times before). In June, he sued the Copyright Office over this, which seems like a long shot.

And now, he’s also lost his appeal of the ruling in the patent case. CAFC, the Court of Appeals for the Federal Circuit — the appeals court that handles all patent appeals — has rejected Thaler’s request just like basically every other patent and copyright office, and nearly all courts.

If you have the time, the August 15, 2022 posting is an interesting read.

Consciousness and ethical AI

Just to make things more fraught, an engineer at Google has claimed that one of their AI chatbots has consciousness. From a June 16, 2022 article (in Canada’s National Post [previewed on epaper]) by Patrick McGee,

Google has ignited a social media firestorm on the the nature of consciousness after placing an engineer on paid leave with his belief that the tech group’s chatbot has become “sentient.”

Blake Lemoine, a senior software engineer in Google’s Responsible AI unit, did not receive much attention when he wrote a Medium post saying he “may be fired soon for doing AI ethics work.”

But a Saturday [June 11, 2022] profile in the Washington Post characterized Lemoine as “the Google engineer who thinks “the company’s AI has come to life.”

This is not the first time that Google has run into a problem with ethics and AI. Famously, Timnit Gebru who co-led (with Margaret Mitchell) Google’s ethics and AI unit departed in 2020. Gebru said (and maintains to this day) she was fired. They said she was ?, they never did make a final statement although after an investigation Gebru did receive an apology. You *can* read more about Gebru and the issues she brought to light in her Wikipedia entry. Coincidentally (or not), Margaret Mitchell was terminated/fired in February 2021 from Google after criticizing the company for Gebru’s ‘firing’. See a February 19, 2021 article by Megan Rose Dickey for TechCrunch for details about what the company has admitted is a firing or Margaret Mitchell’s termination from the company.

Getting back intellectual property and AI.

What about copyright?

There are no mentions of copyright in the earliest material I have here about the ‘creative’ arts and artificial intelligence is this, “Writing and AI or is a robot writing this blog?” posted July 16, 2014. More recently, there’s “Beer and wine reviews, the American Chemical Society’s (ACS) AI editors, and the Turing Test” posted May 20, 2022. The type of writing featured is not literary or typically considered creative writing.

On the more creative front, there’s “True love with AI (artificial intelligence): The Nature of Things explores emotional and creative AI (long read)” posted on December 3, 2021. The literary/creative portion of the post can be found under the ‘AI and creativity’ subhead approximately 30% of the way down and where I mention Douglas Coupland. Again, there’s no mention of copyright.

It’s with the visual arts that copyright gets mentioned. The first one I can find here is “Robot artists—should they get copyright protection” posted on July 10, 2017.

Fun fact: Andres Guadamuz who was mentioned in my posting took to his own blog where he gave my blog a shout out while implying that I wasn’t thoughtful. The gist of his August 8, 2017 posting was that he was misunderstood by many people, which led to the title for his post, “Should academics try to engage the public?” Thankfully, he soldiers on trying to educate us with his TechnoLama blog.

Lastly, there’s this August 16, 2019 posting “AI (artificial intelligence) artist got a show at a New York City art gallery” where you can scroll down to the ‘What about intellectual property?’ subhead about 80% of the way.

You look like a thing …

i am recommending a book for anyone who’d like to learn a little more about how artificial intelligence (AI) works, “You look like a thing and I love you; How Artificial Intelligence Works and Why It’s Making the World a Weirder Place” by Janelle Shane (2019).

It does not require an understanding of programming/coding/algorithms/etc.; Shane makes the subject as accessible as possible and gives you insight into why the term ‘artificial stupidity’ is more applicable than you might think. You can find Shane’s website here and you can find her 10 minute TED talk here.

*’can’ added to sentence on May 12, 2023.