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Is AI on the brink of something massive? Thats been the buzz over the past several months, thanks to the release of improved large language models (LLMs) such as OpenAIs GPT-4, the successor to ChatGPT. Developed as tools for language processing, these algorithms respond so fluently and naturally that some users become convinced they are conversing with a genuine intelligence. Some researchers have suggested that LLMs go beyond traditional deep-learning AI methods by displaying emergent features of the human mind, such as a theory of mind that attributes other agents with autonomy and motives. Others argue that, for all their impressive capabilities, LLMs remain exercises in finding correlations and are devoid not just of sentience but also of any kind of semantic understanding of the world they purport to be talking about as revealed, for example, in the way LLMs can still make absurd or illogical mistakes or invent false facts. The dangers were illustrated when Bings search chatbot Sydney, which incorporated ChatGPT, threatened to kill an Australian researcher and tried to break up the marriage of a New York-based journalist after professing its love.

AI and complexity experts Melanie Mitchell and David Krakauer of the Santa Fe Institute, US, meanwhile, suggest a third possibility: that LLMs do possess a genuine kind of understanding, but one that we dont yet understand ourselves and which is quite distinct from that of the human mind.1

Despite their name, LLMs are not only useful for language. Like other types of deep-learning methods, such as those behind DeepMinds protein-structure algorithm AlphaFold, they mine vast data sets for correlations between variables that, after a period of training, enable them to provide reliable responses to new input prompts. The difference is that LLMs use a neural-network architecture called a transformer, in which the neurons attend more to some of its connections than to others. This feature enhances the ability of LLMs to generate naturalistic text, but it also makes them potentially better able to cope with inputs outside the training set because, some claim, the algorithms deduce some of the underlying conceptual principles and so dont need to be told as much in training.

The inner workings of these networks are largely opaque

Melanie Mitchell and David Krakauer, Santa Fe Institute

This suggests that LLMs might also do better than conventional deep learning when applied to scientific problems. Thats the implication of a recent paper that applied a LLM to the AlphaFold problem of deducing protein structure purely from sequence.2(Im reluctant to call it the protein-folding problem, because thats a little different.) Alphafolds capabilities have been rightly lauded, and theres even some reason to think it can infer some of the features of the underlying energy landscape. But Alexander Rives at Meta AI in New York and his colleagues say that their family of transformer protein language models collectively called ESM-2, and a model called ESMFold derived from it, do even better. The language models are faster by up to two orders of magnitude, need less training data, and dont rely on collections of so-called multiple sequence alignments: sequences closely related to the target structure. The researchers ran the model on around 617 million protein sequences in the MGnify90 database curated by the European Bioinformatics Institute. More than a third of these yield high-confidence predictions, including some that have no precedent in experimentally determined structures.

The authors claim that these improvements in performance are indeed because such LLMs have better conceptual understanding of the problem. As they put it the language model internalises evolutionary patterns linked to structure which means that it potentially opens up a deep view into the natural diversity of proteins. With around 15 billion parameters in the model, it is not yet easy to extract with any certainty what the internal representations are that feed the improvements in performance. But such a claim, if well supported, makes LLMs much more exciting for doing science, because they might work with or even help reveal the underlying physical principles involved.

The authors claim that these improvements in performance are indeed because such LLMs have better conceptual understanding of the problem. As they put it the language model internalises evolutionary patterns linked to structure which means that it potentially opens up a deep view into the natural diversity of proteins. With around 15 billion parameters in the model, it is not yet easy to extract with any certainty what the internal representations are that feed the improvements in performance: The inner workings of these networks are largely opaque, say Mitchell and Krakauer. But such a claim, if well supported, makes LLMs much more exciting for doing science, because they might work with or even help reveal the underlying physical principles involved.

There may yet be a way to go, however. When chemists Cayque Monteiro Castro Nascimento and Andr Silva Pimentel of the Pontifcia Universidade Catlica do Rio de Janeiro in Brazil set ChatGPT some basic chemical challenges, such as converting compound names into Smiles chemical representations, the outcomes were mixed. The algorithm correctly identified the symmetry point groups of six out of ten simple molecules and did a fair job of predicting the water solubility of 11 different polymers. But it did not seem to know the difference between alkanes and alkenes, or benzene and cyclohexene. As with language applications, getting good results here might depend partly on posing the right questions: there is now an emerging field of prompt engineering to do this. Then again, asking the right question is surely one of the most important tasks for doing any kind of science.

Read more here:
Interpreting the impact of AI large language models on chemistry - Chemistry World

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Whether youre looking to make fast, fluffy fries or quick crispy bacon, both air fryers and deep fat fryers are great options. The deep fat fryer is a classic, delivering on familiar taste and texture. Air fryers are becoming increasingly popular, establishing themselves as a kitchen staple.

After extensive research and testing, our expert team has the professional advice to guide you to an informed decision. We've tested the best air fryers on the market. After comparing these products to a classic deep fat fryer, we can give a fair verdict on which youll want to have in your kitchen.

When it comes down to it, an air fryer is better than a deep fat fryer. However, there's a lot to consider before you choose. We've compared both appliances on price, space, and taste to tell you what you need to know before you buy.

Today's best air fryer and deep far fryer deals

(Image credit: GettyImages)

Deep fat fryers heat oil to high temperatures. Once the oil is hot, you plunge your food into the oil, turning it to get an even fry. The cooking itself is quick, but make sure to account for time to heat and cool the oil before and after. Bear in mind that youll need to stay by the fryer the whole time that your food is cooking, too.

Air fryers work with little to no oil. They are smaller machines which rapidly circulate hot air around a basket container to cook your food. The cooking takes a little longer, but it can produce results which have a comparable taste and a similar texture. You wont need to stay near the appliance, because they often have paddles to keep food moving while it cooks. If they dont, the most itll need is a shake or mix half way through.

(Image credit: Future / Alex David)

Results

WINNER: It's a tie

To start, air fryers will only cook battered foods if they're frozen, like breaded chicken or fish. If you want to make food with wet batter like churros, youll need a deep fat fryer. Having tried frozen food, vegetables, and the benchmark for all frying fries, we were pleased with the results of both appliances.

Our team felt that deep frying gave the perfect results, as expected. However, Millie, our air fryer expert, preferred the taste of her air fryer's food. She told us that tthe air fryer and deep fryer produced food which was shockingly similar in taste. The main difference was, when deep-frying, I wasnt able to season my fries until after I had cooked them, which meant that the air fried plate was more flavorful. You could say that the flavors were baked right in during the cooking process.

If there isnt much difference in the way of taste, air fryers might win overall, since they have a lower fat content. However, if you want to make churros, youll need a deep fat fryer.

(Image credit: GettyImages)

Cleaning up

WINNER: Air fryer

A common grievance with deep-fryers is the clear-up process. Oil is tough to clean and, when hot, the fryer will likely spit oil onto your surfaces. Your food will have oil sitting on it after cooking, so youll want some kitchen roll to soak that up.

Once finished with frying, youll need to wait for the oil in your deep fat fryer to cool before either disposing of it, or storing it somewhere. The most common solution is to let your oil cool, pour it into a nonrecyclable container and either keep it, or put it in the garbage. Oil also has a lingering smell, so make sure you ventilate your kitchen.

On the whole, air fryers are easy to clean. They come with removable baskets which are often dishwasher safe. There isnt much oil involved in the process, so it doesnt get as messy as deep frying.

(Image credit: GettyImages)

Cost

WINNER: deep fat fryer

Air fryers tend to have a higher upfront cost than deep fat fryers. You can buy ovens with integrated air fryers if you are looking for value. We love the Instant Pot Duo Crisp with Ultimate Lid for covering multiple functions in one. Deep fryers tend to be less expensive, however, youll need to replace the oil in the deep fryer regularly. An example of a comparable deep fat fryer is the Progress EK2969P Compact Deep Fat Fryer (opens in new tab). Its small and easy to store.

Instant Pot Duo Crisp with Ultimate Lid

We love this because it's so much more than an air fryer. It performed exceptionally, was easy to clean, and had capacity for everything from roast chickens to mash potatoes. We loved that this has 11 different functions, so is an appliance that can do more than air fry.

Progress EK2969P Compact Deep Fat Fryer

Millie, our expert, liked it because it's a competitive size in comparison to air fryers. It's easy to store and doesn't need a huge amount of oil.However, because it is small, the capacity isn't particularly large, so it is really a single-person appliance.

(Image credit: Amazon)

(Image credit: Beautiful Kitchenware)

Size and look

WINNER: air fryer

As air fryers continue to improve, they are getting smaller, more storable, and much slicker. If you want to pack it into a drawer, the Ninja Max XL Air Fryer is a brilliant option. Equally, our team loved the look of Beautiful by Drew Barrymore Touchscreen Air Fryer to leave on your countertop. Deep fryers have less of an aesthetic appeal, but you can buy small ones and stow them away in a cupboard.

The Ninja Max XL Air Fryer can crisp up fries in minutes and is perfectly sized for small households, but its plastic finish lacks refinement

Beautiful 6-Quart Digital Air Fryer

The Beautiful 6-Quart Digital Air Fryer stands out thanks to its attractive design, which will look right at home in any contemporary kitchen.

(Image credit: Cosori)

Our verdict

For me, the air fryer is the clear winner. Even though the upfront cost can be a little more, its easier to store, clean, and use. The taste test really helps the air fryer sit in the top spot for me; its a healthier option, without compromising on flavor or texture. However, if you are looking to make churros and battered food, youll need to buy a deep fryer.

(Image credit: Getty Images)

How we test

We like all of our products to have been fried-and-tested, so we make sure that we have personally used an appliance before reviewing it. Where we haven't tried it, we research and read reviews thoroughly.

We were unable to try a deep fryer, but, luckily, Millie had already tested the T-Fal Actifry Genius + (alongside many others) against a Progress EK2969P Compact Deep Fat Fryer.

When testing, Millie was assessing each appliance on a number of factors:

Noise: Lots of noise doesn't always equate to lots of power and can make it hard to do other things around the house.

Speed: Deep fryers are quicker in cooking time, so it was important to look at how long these appliances would take exactly. Fries would take around 25 minutes in the air fryer, but some on our best air fryer list took 12 minutes.

Looks: These are often on your countertops, so we wanted to make sure that we accounted for how these look. In our roundup, we highlighted the less attractive features, if there were any.

Cleaning: Cleaning an air fryer is advertised as easy. Most baskets can do in the dishwasher. This was a key factor for choosing the air fryer over the deep fryer, so we scrutinised cleaning.

For more insight, our review guidelines explain more about our product review process.

For the most part, yes. Our expert tester, Millie Fender, told us that her partner couldnt tell the difference between most of the foods which she tested in the air fryer and deep fryer. However, if youre being picky, and looking for that guilty-pleasure grease, youll need a deep fat fryer.

That depends on what health means to you. Air fryers are praised for using less oil to cook your food. For example, rather than plunging fries into a deep fat fryer, you will use a tablespoon, at most, of oil in an air fryer. This means that the fat content of your food will be reduced. This is considered to be generally healthier, but that doesnt apply to all people.

Yes, but not homemade batter. You can make bacon, fries, vegetables, and heat up frozen battered food like chicken and fish or fish. However, the air fryer cannot crisp up a wet batter like a deep fryer can.

In many instances, yes. As above, you can do most of the jobs of a deep fryer with an air fryer, including making competitively crispy and fluffy fries.

Air fryers take longer to cook your food. They can take up to twenty minutes where the deep fryer might only take two. That being said, the clean-up process is much faster with an air fryer.

Yes. If you have a deep pot or pan, some oil, and a slotted spoon you can use your home equipment as a fryer. This is a good option for saving on space too.

Vegetable oil, canola oil, and peanut oil are the most popular options. They have a higher smoke point, so are the best oils to use.

People tend to recommend that you change the oil after eight to ten uses. The color and quality of the oil will affect the taste, so it depends how sensitive you are to flavor.

There are lots of benefits to both appliances and you can use them to make some great meals and snacks. Deep fryers are classic and, in many ways, offer you more versatility in what you can fry. However, lots of air fryers are becoming integrated into other multi-cookers, which offer fantastic value for money. Think about space, taste, and price and you won't go wrong.

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Millie Fender is Head of Reviews. She specializes in cooking appliances and also reviews outdoor grills and pizza ovens. She was tasked with reviewing the market leading air fryers, so is our expert on the topic. When she's not putting air fryers, and other appliances, through their paces in our testing kitchen, she'll be using the products at home in her day-to-day life.

More here:
Air Fryer vs Deep Fat Fryer: fried-and-tested by experts - Homes & Gardens

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OpenAI CEO Sam Altman loves Italy, but the affection may not be mutualat least not when it comes to OpenAIs flagship product, ChatGPT.

Italy temporarily banned ChatGPT last week on the grounds that it violates Europes strict data privacy law, GDPR. OpenAI immediately complied with the ban, saying it would work with Italian regulators to educate them on how OpenAIs A.I. software is trained and operates.

We of course defer to the Italian government and have ceased offering ChatGPT in Italy (though we think we are following all privacy laws), Altman tweeted, adding that Italy is one of my favorite countries and I look forward to visiting again soon!

The comments drew plenty of snark from other Twitter users for its slightly tone deaf, ugly American vibes. Meanwhile, Italys deputy prime minster took the countrys data regulator to task, saying the ban seemed excessive. But Romes decision may be just the start of generative A.I.s problems in Europe. As this newsletter was preparing to go to press, there were reports Germany was also considering a ban.

Meanwhile, here in the U.K., where Im based, the data protection regulator followed Italys ban with a warning that companies could very well fall afoul of Britains data protection laws too if they werent careful in how they developed and used generative A.I. The office issued a checklist for companies to use to help ensure they are in compliance with existing laws.

Complying with that checklist may be easier said than done. A number of European legal experts are actively debating whether any of the large foundation models at the core of todays generative A.I. boomall of which are trained on vast amounts of data scraped from the internet, including in some cases personal informationcomply with GDPR.

Elizabeth Renieris, a senior researcher at the Institute for Ethics in AI at the University of Oxford who has written extensively about the challenges of applying existing laws to newly-emerging technology such as A.I. and blockchain, wrote on Twitter that she suspected GDPR actions against companies making generative A.I. will be impossible to enforce because data supply chains are now so complex and disjointed that its hard to maintain neat delineations between a data subject, controller, and processor (@OpenAI might try to leverage this). Under GDPR, the privacy and data protection obligations differ significantly based on whether an organization is considered a controller of certain data, or merely a processor of it.

Lilian Edwards, chair of technology law at the University of Newcastle, wrote in reply to Renieris, These distinctions chafed when the cloud arrived, frayed at the edges with machine learning and have now ripped apart with large models. No-one wants to reopen GDPR fundamentals but I am not clear [the Court of Justice of the European Union] can finesse it this time.

Edwards is right that theres no appetite among EU lawmakers to revisit GDPRs basic definitions. Whats more, the bloc is struggling to figure out what to do about large general-purpose models in the Artificial Intelligence Act it is currently trying to finalize, with the hope of having key EU Parliamentary committees vote on a consensus version on April 26. (Even then, the act wont be really be finalized. The whole Parliament will get to make amendments and vote in early May and there will be further negotiation between the Parliament, the EU Commission, which is the blocs executive arm, and the European Council, which represents the blocs various national governments.) Taken together, there could be real problems for generative A.I. based on large foundation models in Europe.

At an extreme, many companies may have to follow OpenAIs lead and simply discontinue offering these services to EU citizens. It is doubtful European politicians and regulators would want that outcomeand if it starts to happen, they will probably seek some sort of compromise on enforcement. That alone may not be enough. As has been the case with GDPR and trans-Atlantic data sharing, European courts have been quite open to citizens groups going to court and obtaining judgements based on strict interpretations of the law that force national data privacy regulators to act.

At a minimum, uncertainty over the legal status of large foundation models may make companies, especially in Europe, much more hesitant to deploy them, especially in cases where they have not trained the model from scratch themselves. And this might be the case for U.S. companies that have international operations tooGDPR applies not just to customer data, but also employee data, after all.

With that, heres the rest of this weeks news in A.I.

Jeremy Kahn@jeremyakahnjeremy.kahn@fortune.com

U.K. government releases A.I. policy white paper. The British governments Department for Science, Innovation and Technology published a white paper on how it wants to see A.I. governed. It urges a sector and industry-specific approach, saying regulators should establish tailored, context-specific approaches that suit the way A.I. is actually being used in their sectors, and for applying existing laws rather than creating new ones. The recommendations also lay out high level principles in five main areas: safety, security, and robustness; transparency and explainability; fairness; accountability and governance; and contestability and redress. While some A.I. and legal experts praised the sector-specific approach the white paper advocates, arguing it will make the rules more flexible than a one-size-fits-all approach and promote innovation, others worried that different regulators might diverge in their approach to identical issues, creating a confusing and messy regulatory patchwork that will actually inhibit innovation, CNBC reported.

Bloomberg creates its own LLM, BloombergGPT, for finance. Bloomberg, where I worked before coming to Fortune, is not new to machine learning. (Ive periodically highlighted some of the ways Bloomberg has been using large language models and machine learning in this newsletter.) The company has access to vast amounts of data, much of it proprietary. This past week, Bloomberg unveiled Bloomberg GPT, a 50 billion parameter LLM, and the first ultra-large language GPT-based model the financial news company has ever trained. This puts it pretty far up there in the rankings of large models, although still far smaller than the largest models OpenAI, Google Brain, DeepMind, Nvidia, Baidu and some other Chinese researchers have built. The interesting thing is that 51% of the data Bloomberg used was financial data, some of it its own proprietary data, that it curated specifically to train the model. The company reported that BloombergGPT outperformed general-purpose LLMs on tasks relevant to Bloombergs own use cases, such as recognizing named entities in data, performing sentiment analysis on news and earnings reports, and answering questions about financial data and topics. Many think this is a path many large companies with access to lots of data will choose to take going forwardtraining their own proprietary LLM on their own data and tailored to their own use casesrather than relying on more general foundation models built by the big tech companies.

Research collective creates open-source version of DeepMind visual language model as step towards an open-source GPT-4 competitor. The nonprofit A.I. research group LAION released a free, open-source version of Flamingo, a powerful visual language model created by DeepMind a year ago. Flamingo is a fully multi-modal model, meaning it can take in both images, videos, and text as inputs and output in all those modes too. That enables it to describe images and also answer questions about them, as well as generating images (or possibly video) from text, similar to the way Stable Diffusion, Midjourney and DALL-E can. Flamingo had some interesting twists in its architecture that enable it to do thisincluding a module called a perceiver remixer that reduces complex visual data to a much lower number of tokens to be used in training, the use of a frozen language model, and other clever innovations you can read about in DeepMinds research paper.

Any way, LAION decided to copy this architecture, apply it to its own open-source, multi-modal training data and the result is Open Flamingo.

Why should you care? Because LAION explicitly says it is doing this in the hopes that someone will be able to use Open Flamingo to train a model that essentially replicates the capabilities of GPT-4 in its ability to ingest both text and images. This means everyone and anyone might soon have access to a model as powerful as OpenAIs currently most powerful A.I., GPT-4, at essentially no cost. That could either be a great thing or a terribly dangerous thing, depending on your perspective.

And another subtle dynamic here that doesnt often get discussed: One of the things that is continuing to drive OpenAI to release new, more powerful models and model enhancements (such as the ChatGPT plugins) so quickly is the competition it is facing not just from other tech players, such as Google, but the increasingly stiff competition it faces from open source alternates. These open source competitors could easily erode the marketshare OpenAI (and its partner Microsoft) was hoping to control.

In order to maintain a reason for customers to pay for its APIs, OpenAI is probably going to have to keep pushing to release bigger, more powerful, more capable modelswhich, if you believe these models can be dangerous (either because they are good for producing misinformation at scale, or because of cybersecurity risks, or because you think they just might hasten human extinction, then anything that incentivizes companies to put them out in the world with less time for testing and for installing guardrails, is probably not a good thing).

ChatGPT gave advice on breast cancer screenings in a new study. Heres how well it didby Alexa Mikhail

Former Google CEO Eric Schmidt says the tech sector faces a reckoning: What happens when people fall in love with their A.I. tutor?by Prarthana Prakash

Nobel laureate Paul Krugman dampens expectations over A.I. like ChatGPT: History suggests large economic effects will take longer than many people seem to expectby Chloe Taylor

Google CEO wont commit to pausing A.I. development after experts warn about profound risks to societyby Steve Mollman

How should we think about the division over last weeks open letter calling for a sixth month pause in the development of any A.I. system more powerful than GPT-4? I covered some of this in Fridays special edition of Eye on A.I. But theres a very nice essay on how politicized discourse over A.I. risks is becoming, from VentureBeats A.I. reporter, Sharon Goldman. Its worth a read. Check it out here.

Also, how should we feel about Sam Altman, the OpenAI CEO, who claims to be both a little bit frightened about advanced A.I. and, simultaneously, hellbent on creating it? Well, dueling profiles of Altman, one in the New York Times and one in the Wall Street Journal, try to sort this out. Both are worth a read.

The cynical take on Altman was put forth by Brian Merchant in an op-ed in the Los Angeles Timesnamely, that fear-mongering about A.I., particularly about its ability to replace lots of peoples jobs, only serves to hype the power of existing technologies and OpenAIs brand, boosting its sales.

I agree with some of Merchants take. I do think OpenAI has very much become a commercially-motivated enterprise, and that this explains a lot about why it is releasing powerful A.I. models so quickly and why it has done things like create the ChatGPT plugins. But, Im not sure about Merchants take on Altman himselfthat Altmans conflicted genius schtick is simply that, schtick. Altmans concern with A.I. Safety is not some newfound preoccupation that came about only once he had something to sell. Its clear from those Altman profiles that AGI and its potential for good and ill have been preoccupations of Altmans for a long time. Its what led him to cofound OpenAI with Elon Musk in the first place. And remember, when it started, OpenAI was just a nonprofit research lab, dedicated to open sourcing everything it did. Altman didnt set out to run a commercial venture. (He may have thought there would be money to be made down the line, but making money doesnt seem to have been his real rationale. He was already enormously wealthy at the time.) So I think Altmans simultaneous expressions of longing for AGI and fear of it are not just about hyping A.I. Im not saying the rationale is noble. I just dont think commercial motives explain Altmans strange stance on advanced A.I. I think it has a lot more to do with ego and with a kind of messiah complexor at the very least, a kind of messianic thinking.

In fact, a lot of stuff people who believe in AGI say only makes sense if viewed in religious terms. AGI believers are a lot like evangelicals waiting for the rapture. They both want the second coming and wish to hasten its arrival, and yet on some level they fear it. And while some of these folks are cynical in their beliefsthey only talk about the Armageddon because they have Bibles to sell (that would be Merchants take)others are sincere believers who really do want to save souls. That doesnt mean you have to agree with these folks. But intentions do make a difference. Which do you think Altman is: Bible salesman or modern day prophet?

See more here:
For ChatGPT creator OpenAI, Italys ban may just be the start of trouble in Europe - Fortune

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