When AI can taste

Mushrooms on toastIt’s interesting as AI starts to encroach on the physical world via it’s incorporation with Robots. The information that’s involved in moving an object, identifying it and being able to discuss it, it’s importance and also place it say in a structure that is visually pleasing or technically correct. That’s an incredible amount of information and a number of concepts. AI is starting to juggle all of this.

This whole thing of digitising the physical environment. I think AI is about to teach us a lot – and that we have a lot to learn about not just physical space – but what about when an AI can taste? Has the ability to identify any wine and any vintage? The ability to de construct a flavour, a chemical ,a smell.

Hook an AI up to machines that can perform Raman Spectroscopy, Infrared Spectroscopy, fluorescent microscopy and polarized light microscopy and you have the ultimate machine for the verification of historic paintings.

Then hook an AI up to a quantum computer and something like the James Web telescope. Also may be it can also help with us finally sorting out Nuclear fusion. Humanity is really on the cusp of something, I just hope we can make sure it works well for as many of us as possible.

And of course this morning I ran the question thru chat GPT 4 which replied with.

AI in Chemical Analysis

  • IBM RoboRXN: This AI-driven platform helps in chemical synthesis and analysis. It uses natural language processing to interpret chemical procedures and executes them using automated lab equipment. Learn more about IBM RoboRXN here and here.
  • DeepChem: An open-source toolkit developed to apply deep learning to the field of chemistry. It is used for tasks such as molecular property prediction, quantum chemistry, and bioactivity prediction. Check out DeepChem on GitHub.
  • MoleculeNet: A benchmark suite for molecular machine learning, which provides various datasets for training AI models in chemical analysis. Explore MoleculeNet on its GitHub repository.

AI and Taste

  • Gastrobots: These are robots designed to taste and analyze food. Researchers have developed AI systems integrated with electronic tongues to evaluate flavors and ingredients in food products.
  • Project “Chefling” by IBM: This project uses AI to generate new recipes and analyze flavor profiles by understanding the chemistry of ingredients and their combinations. More about IBM’s AI initiatives can be found on their research blog.

AI and Smell

  • Digital Olfaction: Companies like Aryballe are developing digital olfactory systems that use AI to identify and classify different smells. These systems have applications in quality control, environmental monitoring, and even in the food and beverage industry. More about Aryballe’s work can be found on their website.
  • Google Brain’s Perfume Project: This initiative focuses on using machine learning to create new fragrances by understanding and predicting how different chemical combinations produce specific scents. More about Google’s AI research can be found on their AI blog.

AI in Pharmaceutical Chemistry

  • Insilico Medicine: This company uses AI for drug discovery and design. It employs deep learning algorithms to predict the effectiveness and potential side effects of new compounds, accelerating the drug development process. Visit their website for more information.
  • BenevolentAI: This project leverages AI to analyze vast amounts of biochemical data to identify new drug candidates and therapeutic targets. Learn more about their projects on their official site.

Summing up AI 2023

So the last 12 months have been amazing, if not rather dramatic with regards to AI. Things have improved a lot and we will see and hear more of this over 2024 I’m sure.

These are some of the things that I’ve found interesting…

We of course have had the big dwrama over at openAI, with Sam Altman being fired / Quitting? and then the board being fired and Sam getting his job back. Rolling stone has an interesting write up about this.

Everyone is wondering and predicting what this Q* (Pronounced Q star) product at open AI is – some think it may be an AGI (Artificial General Intelligence) but very few people have had access to this product so far. Although there is a lot of speculation.

We still don’t know what’s happened with googles LAMBDA and Blake Lemoine is still I think the canary in the coal mine with regards this technology.

The issue of building your own “Bad version of chatGPT” is a very real possibility. We should beware of Bad Robots! And I mean Bad Robots- you could theoretically hook your own bent version of chatGPT up to a mechanical device and let it lose(Who knows what the military are up to with this idea).

In addition to this is the issue of copyright and the fact that everyone is ignoring the importance of related links and knowledge that back up the statements made by AI (not to mention the issue of AI hallucinations) . Although it is possible for these platforms to supply and reference sources, most of the commercial products don’t include this functionality. This I think is going to pan out in interesting ways. Already a number of Authors are attempting to sue OpenAI.

But I think the most interesting thing you could do is build your own chatGPT and train it on your own data. I’ve set up something on an old machine I run my self and gave it a number of my old blog articles and various other bits and bobs to play with. The results were solid and interesting (with references!).

But two things come to mind with regards this. You need CPU and Ram and ideally a few GPU’s to run this sort of software. In short a grunty machine or an expensive virtual machine, that runs at a reasonable speed (although I did manage to get this running on a machine with 4 cores, 8 gig of ram – it was very slow) but the scenario that comes to mind is this.

If you have your own company and fast access to your own data (files, emails databases, financial data etc), and can hook it up to your machine, you could probably gain all sorts of interesting insight. What was the most profitable project? How many emails were sent? What were the time frames for this project. These and a whole lot more questions could be asked about your data. The stinger comes though when you get around to the speed of the computer running this and the connectivity of your expensive AI brain to the content.

If you only have a 100 megabit to all that data sitting in the cloud, it’s going to slow things down. If you have invested in local hardware (and say have 10 gigabit or more connectivity) to your data your going to get results much much more quickly. I see an argument for employing your own sysadmin percolating!

In short 2024 is going to be just as crazy as 2023 it’s sort of amazing to be alive and witnessing all this. Thanks for reading and stay safe over the holiday season!



Related links quoted! _____________________________

Blake Lemoine

WTF Is Happening at OpenAI?

This new AI is powerful and uncensored… Let’s run it

Authors sue OpenAI over ChatGPT copyright: could they win?

Let’s build GPT: from scratch, in code, spelled out.


Ai for work and home

As AI is starting to become something many of us are using, it’s interesting to think about the possibility’s of getting an AI to look at our own data.  I’ve set up privateGPT on an older computer and given it data to “ingest”!

It does take some time and it needs a decent amount of ram and cpu grunt, but it does work surprisingly well even on under-powered machines. It also gives you links to source documents you may have got the device to ingest (unlike some products I might mention… cough!).

There is a part of me that thinks that this sort of product could be very useful for insite for a private company (or even an individual). Think about the possibilities of giving it access to all emails sent. What could it learn? All files on the server, who created the most files? Who was involved in which projects and what were the  skill sets of the people involved (past and present).

Lets also think about the possibility of an AI having access to production data and financials!? What could be gleaned from all that information. It’s been said that we will soon have personal versions of AI that can run on phones. I’m sort of looking forward to this … but having recently re watched the most excellent Ridley Scott movie movie Alien Covenant, I’m recommending that we proceed with caution!


AI secrete source!

A couple of the publicly accessible AI’s that I’ve been tinkering with will not quote sources and will not tell you much if anything with regards what they have been trained on. But there is now this very interesting development that the Guardian have reported on which is that Sarah Silverman  is current sueing OpenAI and Meta claiming AI training infringed copyright


I’m going to be holding on to my pop corn real tight as this works it’s way thru the courts, and the big wigs in silicon valley work out what to do and how to do it. I told you 2023 was going to be a very interesting year for AI.