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

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.

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Ontario poll shows deep dissatisfaction with Ford government despite high party support – Global News

Premier Doug Fords government is receiving poor marks for its handling of nearly all of the issues that are top of mind for Ontarians, according to a new public opinion poll.

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The Angus Reid survey of 881 Ontario residents found that while a majority would still vote for the Progressive Conservatives if an election were to be held today, theres an underlying dissatisfaction with how the government is performing.

If an election was held today, 38 per cent of respondents said they would vote for Doug Fords PC Party and 30 per cent said they would support the Ontario NDP.

Support for the leaderless Ontario Liberals dropped to 20 per cent and Ontario Greens remain steady at six per cent of the total projected vote.

The poll, however, is less encouraging when it comes to key issues such as cost of living, housing affordability and health care.

A total of 83 per cent of those polled felt the government was doing a poor or very poor job on the issue of housing affordability, with 81 per cent critical of Ontarios record on the cost of living and inflation.

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Health care an area the Ford government focused a flurry of announcements and new legislation on at the start of the year did not fare much better.

A total of 78 per cent of those polled felt the province had done a poor or very poor job on the health care file, compared to 19 per cent who felt it was good or very good.

Of all the issues polled, Ontarians appear to have the best impression of the Ford governments relationship with Ottawa, with just 47 per cent responding with poor or very poor.

Angus Reid suggested that even the provinces reported victories may not be registering much public support.

Its poll found just 37 per cent felt the government was doing a good job on the economy and job creation. This, after the province announced a Volkswagen gigafactory would open in St. Thomas, Ont. in 2027, the polling group said.

© 2023 Global News, a division of Corus Entertainment Inc.

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For ChatGPT creator OpenAI, Italys ban may just be the start of trouble in Europe – Fortune

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?

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The future, one year later – POLITICO – POLITICO

In this Oct. 30, 2008, photo, Electric Time Company employee Dan Lamoore adjusts the color on a 67-inch square LED color-changing clock at the plant in Medfield, Mass. | Elise Amendola/AP photo

When this newsletter launched exactly one year ago today, we promised to bring you a unique and uniquely useful look at questions that are addressed elsewhere as primarily business opportunities or technological challenges.

We had a few driving questions: What do policymakers need to know about world-changing technologies? What do tech leaders need to know about policy? Could we even get them talking to each other?

Were still working on that last one. But what we have brought you is a matter of public record: Scoops on potentially revolutionary technologies like Web3, a blow-by-blow account of the nascent governing structure of the metaverse and a procession of thinkers on the transformation AI is already causing, and how we might guide it.

Yeah, about that. In just a year, AI has gone from a powerful, exciting new technology still somewhat on the horizon to a culture-and-news-dominating, potentially even apocalyptic force. Change is always happening in the tech world, but sometimes it happens fast. And as the late Intel chief Gordon Moore might have said, that speed begets more speed, with seemingly no end in sight.

The future already looks a lot different than it looked in April 2022. And we dont expect it to look the same next year, or next month, or even next week. Theres a lot of anxiety that AI in particular could change the future much, much faster than were ready to address.

With that in mind I spoke yesterday with Peter Leyden, founder of the strategic foresight firm Reinvent Futures and author of The Great Progression: 2025 to 2050 a firmly optimistic reading of how technology will change society in radical ways about how the rise of generative AI has shaken up the landscape, and what he sees on the horizon from here.

This is the kind of explosive moment that a lot of us were waiting for, but it wasnt quite clear when it was going to happen, Leyden said. Ive been through many, many different tech cycles around, say, crypto, that havent gone down this path this is the first one that is really on the scale of the introduction of the internet.

Tech giants have been spending big on AI for more than a decade, with Googles acquisition of DeepMind as a signal moment. Devoted sports viewers might remember one particularly inescapable 2010s-era commercial featuring the rapper Common proselytizing about AI on Microsofts behalf. And there is, of course, a long cultural history of AI speculation, dating back to James Camerons Terminator and beyond.

There is a kind of parallel to the mid-90s, where people had a very hard time understanding both the digitization of the world and the globalization of the world that were happening, Leyden said. Were seeing a similar tipping point with generative AI.

From that perspective, the current generative AI boom begs for a historical analogue. How about America Online? It might seem hopelessly dated now, but like ChatGPT it was a ubiquitous product that brought a revolutionary technology into millions of homes. From the perspective of 20 years from now, a semi-sophisticated chatbot might seem like the Youve got mail of its time.

AI might seem a chiefly digital disruptor right now, but Leyden, who has a pretty good track record as a prognosticator, believes it could revolutionize real-world sectors from education to manufacturing to even housing.

Weve always thought those things are too expensive and cant be solved by technology, and weve finally now crossed the threshold to say Oh wait, now we could apply technology to it, Leyden said. The next five to 10 years are going to be amazing as this superpower starts to make its way through all these fields.

AI is also already powering innovation in other fields like energy, biotech, and media. Thats where its an especially salient comparison with the internet as a whole, not just a platform like social media. Its an engine, not the vehicle itself, and there are millions of designs yet to be built around it.

Largely for that reason, its nearly impossible to predict whats going to happen next with AI. Maybe artificial general intelligence really will arise, posing an entirely different set of problems than the current policy concerns of regulating bias and accountability in decision-making algorithms. Or maybe it will start solving problems, wickedly difficult ones, like nuclear fusion and mortality and space survival.

To get back to our mission here: We cant know. What we can do is continue to cover the bleeding edge of these technologies as they exist now, and where the people in charge of building and governing them aim to steer their development and, by proxy, ours.

A message from TikTok:

TikTok is building systems tailor-made to address concerns around data security. Whats more, these systems will be managed by a U.S.-based team specifically tasked with managing all access to U.S. user data and securing the TikTok platform. Its part of TikToks commitment to securing personal data while still giving the global TikTok experience people know and love. Learn more at http://usds.TikTok.com.

A pair of George Mason University technologists are recommending the government take a novel, deliberate approach to AI regulation.

In an essay for GMUs Mercatus Center publication Discourse, Matthew Mittelsteadt and Brent Skorup propose a framework they call AI Progress, a novel framework to help guide AI progress and AI policy decisions. Their big ideas, among a handful of others:

People will need time to understand the limitations of this technology, when not to use it and when to trust it (or not), they write nearing their conclusion. These norms cannot be developed without giving people the leeway needed to learn and apply these innovations.

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Health and tech heavy hitters are teaming up to make their own recommendations about how AI should be used specifically in the world of health care.

As POLITICOs Ben Leonard reported today for Pro subscribers, the Coalition for Health AI, which includes Google, Microsoft, Stanford and Johns Hopkins, released a Blueprint for Trustworthy AI that calls for high transparency and safety standards for the techs use in medicine.

We have a Wild West of algorithms, Michael Pencina, coalition co-founder and director of Duke AI Health, told Ben. Theres so much focus on development and technological progress and not enough attention to its value, quality, ethical principles or health equity implications.

The report also recommends heavy human monitoring of AI systems as they operate, and a high bar for data privacy and security. The coalition is holding a webinar this Wednesday to discuss its findings.

Stay in touch with the whole team: Ben Schreckinger ([emailprotected]); Derek Robertson ([emailprotected]); Mohar Chatterjee ([emailprotected]); Steve Heuser ([emailprotected]); and Benton Ives ([emailprotected]). Follow us @DigitalFuture on Twitter.

If youve had this newsletter forwarded to you, you can sign up and read our mission statement at the links provided.

A message from TikTok:

TikTok has partnered with a trusted, third-party U.S. cloud provider to keep all U.S. user data here on American soil. These are just some of the serious operational changes and investments TikTok has undertaken to ensure layers of protection and oversight. Theyre also a clear example of our commitment to protecting both personal data and the platforms integrity, while still allowing people to have the global experience they know and love. Learn more at http://usds.TikTok.com.

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Is 2023 The Year Of Quantum Computing Startups And A 1 Million Qubit Machine? – Yahoo Finance

Quantum computing uses quantum mechanics to perform operations. Quantum mechanics is a physics theory that describes the physical environment at an atomic and subatomic scale, compared to traditional physics, which looks at the macroscopic scale.

Bits denote data in classical computing. These bits are two-state, the familiar 1 or 0. With quantum computing, quantum bits qubits measure computing power. These exist in multiple states at the same time, which can include combining 0 and 1 simultaneously.

Dont Miss: The Startup Behind The Automated Future Of The Fast-Food Industry

The benefits of this new computing technology include storing massive amounts of information in fewer computers while using less energy. And, by operating outside the traditional laws of physics, quantum computers can offer processing speeds millions of times faster than traditional computers.

In 2019, for example, Googles latest quantum computer performed a calculation in four minutes. The worlds most powerful supercomputer at the time would have needed 10,000 years to finish that same calculation. With 300 qubits, a quantum computers calculations at a given time are greater than the atoms in the universe.

The speed of quantum computers brings many use cases, including faster and smarter artificial intelligence (AI) platforms, advanced pharmaceutical modeling, more accurate weather predictions and the creation of new materials.

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Research firms like Contrive Datum Insights see massive quantum computing market growth. The company projects a compound annual growth rate of 36.89% from 2023 to 2030, with the market reaching $125 billion annually. Where there is that kind of growth and money involved, startups are sure to follow. With quantum computing still in the early stages, startups are tackling multiple fronts, including different computer production methods, advanced quantum algorithms and other innovations.

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Here are some of the quantum computing startups making noise in the space:

Maryland-based quantum computing hardware and software firm IonQ Inc. (NYSE: IONQ). The company partners with various firms like Hyundai Motor Co. to create better machine learning algorithms to improve safety and bring about self-driving automobiles. Hyundai is also leveraging IonQ to study lithium chemistry and find new reactive solutions for future electric vehicles (EVs).

PSIQuantum is a company developing a method of quantum computing that uses photos that represent qubits. The startup is on the CB Insights list of unicorn companies with a current valuation of $3.15 billion as of March 10. The firm completed a $450 million investment round in the summer of 2021 and continues toward its stated goal of developing a 1 million qubit computer.

French startup PASQAL offers quantum computers built with 2D and 3D arrays of ordered neutral atoms, enabling its clients to solve challenging problems. These include improving weather forecasting, boosting auto aerodynamics for greater efficiency and finding relationships between chemical compounds and biological activity for the healthcare industry.

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Established technology giants are also pushing forward quantum computing. IBM remains at the forefront. In November 2022, the company announced the creation of a 430-qubit machine named Osprey, which has the largest qubit count of any processor. IBMs breakthroughs in quantum computing mirror the trajectory of innovation for traditional computers as processing speed increased year over year.

Amazon Inc. Braket is the companys managed quantum computing service and part of its overall growth strategy with Amazon Web Services (AWS). Bracket offers users a place to build, test and run quantum algorithms. It provides them with access to different types of quantum hardware, encourages software development through the Braket SDK and to create open-source software.

Microsoft Corp., Alphabet Inc.s Google, Intel Corp. and Nvidia Corp. also offer quantum computing solutions and investment. As the biggest tech firms increase participation in quantum computing, more startups should become acquisition and merger targets as the market moves toward consolidation.

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Researchers achieve key milestone in move toward commercial … – China Daily

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Quantum computing could reshape how we solve complex problems and process sums of data previously thought impossible to handle.

What could take today's computers thousands of years to solve, quantum computers could potentially calculate in seconds.

This is possible through exploiting the unique capabilities of quantum particles (or qubits) to be able to be in two places at once, and communicate mysteriously with each other even if they are millions of miles apart.

Everything from producing more efficient engines to simulating chemical reactions for developing new medicine, more powerful computing could lead to a plethora of innovation breakthroughs across the scientific disciplines and technology.

As promising as this sounds, building practical quantum computers has been tricky for engineers. Getting qubits to move between quantum chips fast and accurately has always been a major obstacle.

In February, researchers from the University of Sussex in the United Kingdom announced a breakthrough, after managing to solve this problem by cleverly using electrical fields. Quantum information was transferred between chips at record speed with an accuracy of over 99 percent.

By demonstrating that two quantum computing chips can be connected opens the way to scalability, as it means chips can be linked together, like a jigsaw, to create powerful processors.

Proving that this is possible is a major step forward in building machines that can perform functional computations using the technology.

Companies such as Google and IBM have been attempting to engineer simple quantum computers for decades now, at a slow pace. Transferring information between chips has proven difficult, especially when trying to transfer data from one point to another fast and reliably without inducing errors.

Simple quantum computations can be performed in laboratory settings, but in the real world such technology will need to operate in imperfect and unpredictable environments.

Anything from fluctuations in voltage to stray electromagnetic fields from other surrounding devices could all throw the delicate balance of quantum particles out of balance.

When dealing in the realm of the subatomic, delicacy is key, and so breakthroughs such as these could soon lead to further understandings in tapping into quantum processing technology.

Many challenges remain before quantum computing promises to unlock more secrets of reality for scientists.

Quantum computers need to be kept at an extremely cold temperature of absolute zero to minimize interference, which can cause issues when they enter mainstream research facilities. Keeping conditions stable enough for subatomic particles to work their magic is extremely challenging, and the technology is still very much in its early stages.

Slow progress is being made, and however primitive their current state is, their future potential is a worthy incentive.

When the first transistor for traditional modern computing was made in 1947, nobody could predict the impact it would have in the decades to come, with the use of smartphones and laptops just over half a century later.

The belief that quantum computing will also lead to disruptive technologies in the near future still motivates scientists to keep pushing forward. How long it may take to reach this stage, however, is something nobody is certain about.

Predicting future technologies is always difficult, and many technologies go through bursts of advancement and stagnation.

Progress in battery energy storage for example, has remained relatively stuck for many years now, which has in turn held back many other areas of innovation.

Our understanding in genetics and gene editing however, has undergone a renaissance in the last ten years, with new stem cell treatments for cancer such as Car-T therapies now available that would have been impossible even 15 years ago.

The hope is that quantum computing will follow the lead of the latter, and offer us new insights into how we can further innovation across scientific disciplines.

Barry He is a London-based columnist for China Daily.

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VMware’s Lewis Shepherd Joins Technical Advisory Board of … – ExecutiveBiz

Lewis Shepherd, senior director of research and emerging technologies strategy at VMware, was added to the technical advisory board of Quantum Computing Inc.

The executive will draw from his more than three decades of government and industry experience in research and development innovation to provide QCI with product visibility, market intelligence and insight, the quantum computing company said Tuesday.

Aside from his responsibilities at VMware, Shepherds career includes time serving at the Defense Intelligence Agency as a senior executive, the Department of Defense as a special government employee and senior adviser, the Federal Communications Commission as a member of its Technological Advisory Council and at Microsoft as general manager and director.

My plan is to add another four to five professionals to the Board whose expertise span a variety of different touch points to quantum, but with the same passion and tireless work-ethic of Lewis, commented Jim Simon, Jr., chair of the technical advisory board at QCI.

Shepards appointment is the third for the QCI board.

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New evidence that quantum machine learning outperforms classical … – UBC Faculty of Science

Quantum Computing Concept Image.

Quantum machine learning models can achieve quantum advantage by solving a complex class of mathematical problems impossible to crack with a classical computer, according to new research by UBC material scientists.

UBC Blusson Quantum Mater Institute (Blusson QMI) investigator Professor Roman Krems said the results rigorously prove that quantum machine learning does indeed offer the quantum advantage.

The key goal now is to find a real-world machine learning application thatwould benefit from this quantum advantage in practice, said Professor Krems, senior author on the Nature Communications study.

Quantum advantage refers to the instances where quantum computers outperform their classical counterparts when scaling to enormous datasets containing countless variables.

Blusson QMI PhD student and first author of the paper Jonas Jger said the models have universal expressiveness in that they solve not just one problem, but capture the complexity of an entire class of problems that are too complicated to solve with classical machine learning.

While quantum machine learning is often considered to be one of the most promising use cases of quantum computing, there are only a few rigorous results about its real computational advantages, Jger said. Our results offer theoretical guarantees that such advantages indeed exist.

The study proves a quantum advantage exists for two of the most popular quantum machine learning classification models: Variational Quantum Classifiers (also known as quantum neural networks) and Quantum Kernel Support Vector Machines.

We can now confidently explore important real-world applications and develop effective approaches for building informative data encoding quantum circuits that could unlock the full potential of quantum machine learning, said Jger.

The advantages reported in the study are somewhat subject to the quality of the datasets presented to the system. As quantum computing is still in the experimental stage, a challenge faced by researchers is encoding the classical data for processing by a quantum device.

The mathematical problem that weve solved using these models is quite abstract and doesnt have many practical applications. But, because it presents such special properties under the complexity theory, it can be used by others as a benchmark to test how different quantum machine learning models perform, Jger said.

Jger joined UBC in Sept 2022 to commence his PhD studies under the supervision of Professor Roman Krems from UBCs Department of Chemistry and Professor Michael Friedlander from UBCs Computer Science Department.

Professor Krems and his team work at the intersection of quantum physics, machine learning and chemistry on problems of relevance to quantum materials and quantum technologies, including quantum computing, quantum sensing and quantum algorithms.Meanwhile, Professor Friedlander and his research group develop theories and algorithms for mathematical optimization and its applications in machine learning, signal processing and operations research.

Jger hopes to take advantage of their combined expertise to push the limits of quantum computing and develop algorithms that can harness its power for practical applications.

We can now confidently explore important real-world applications and develop effective approaches for building informative data encoding quantum circuits that could unlock the full potential of quantum machine learning.

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Quantum Computing Inc. Announces 2022 Financial Results and Starts Transition to Commercialization – Quantum Computing Report

Quantum Computing Inc. (QCI) reported 2022 total revenue at $135,648 versus no revenue in 2021. Operating expenses were $36.5 million versus $17.1 million in the prior year due to impact of its merger with QPhoton, increase in engineering personnel, non-stock based compensation, and other factors. The net loss was $38.5 million versus $10.7 million in the prior year. The company ended the year with Cash and Cash Equivalents of $5.3 million versus $16.7 million at the end of 2021. After the end of the year, the company has received $6.4 million from sales of $3 million of their shares via an at-the-market facility managed by Ascendiant Capital.

2022 was a pivotal year for the company due to their acquisition of QPhoton which allowed them to offer Quantum Computing as a Service (QCaaS) with a full-stack quantum computing capability. The company has been working on several proof-of-concept projects including projects to optimize sensor placement on a BMW automobile, optimize flight trajectories with VIPC, detect fraudulent banking transactions with Rabobank, optimize windmill placement, optimize nuclear fuel rod replacements, and predict stock performance. They also created a new subsidiary QI Solutions, Inc. to pursue government business.

The company also indicated their roadmap for product development including a Dirac-2 follow-on to the existing Dirac-1 that supports calculations based upon Qudits (0-53 variables) instead of Qubits, a Reservoir Quantum Computer, a Quantum Random Number generator, and other products based upon quantum photonics. The companys goal is to hit EBITDA and cashflow breakeven within 2 years at a revenue level of about $30 million.

For more information about QCIs financial report, you can view their press release posted on their website here.

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IonQ Releases Their Q4 and Fully Year 2022 Financial Results – Quantum Computing Report

IonQ showed continued growth in revenue achieving $3.8 million in the fourth quarter versus $2.8 million in the third quarter and $1.6 million in the fourth quarter of 2021. For the full year, they achieved a total of $11.1 million versus $2.1 million in 2021. Bookings in 2022 were at $24.5 million portending more growth in 2023 with an estimate of revenue between $18.4 to $18.8 million for the full year. Net loss in Q4 came in at $18.6 million versus $23.9 million in Q3 and $74 million in Q4 2021. For the full year the company showed a loss of $48.5 million versus a loss of $106 million in 2021. The company ended the year with $537 million in cash, cash equivalents, and investments compared to $603 million at the end of 2021. The company is benefiting from the large infusions of cash it received from its SPAC merger in October 2021.

The company also summarized key commercial and technical highlights for the year including the acquisition of Entangled Networks, plans to construct a quantum computing manufacturing center in Bothell, Washington, improvements in the performance of their Aria processor to achieve an Algorithmic Qubit level of 25, and several customer collaborations including those with Hyundai Motors, Accenture, and the Irish Centre for High End Computing.

A press release announcing IonQs financial results has been posted on their website here and a replay of their Fourth Quarter and Full Year 2022 Earnings Call can be accessed by filling out a registration form here.

March 31, 2023

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