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MIT’s ‘cyber-agriculture’ optimizes basil flavors – TechCrunch



The days when you could simply grow a basil plant from a seed by placing it on your windowsill and watering it regularly are gone — there’s no point now that machine learning-optimized hydroponic “cyber-agriculture” has produced a superior plant with more robust flavors. The future of pesto is here.

This research didn’t come out of a desire to improve sauces, however. It’s a study from MIT’s Media Lab and the University of Texas at Austin aimed at understanding how to both improve and automate farming.

In the study, published today in PLOS ONE, the question being asked was whether a growing environment could find and execute a growing strategy that resulted in a given goal — in this case, basil with stronger flavors.

Such a task is one with numerous variables to modify — soil type, plant characteristics, watering frequency and volume, lighting and so on — and a measurable outcome: concentration of flavor-producing molecules. That means it’s a natural fit for a machine learning model, which from that variety of inputs can make a prediction as to which will produce the best output.

“We’re really interested in building networked tools that can take a plant’s experience, its phenotype, the set of stresses it encounters, and its genetics, and digitize that to allow us to understand the plant-environment interaction,” explained MIT’s Caleb Harper in a news release. The better you understand those interactions, the better you can design the plant’s lifecycle, perhaps increasing yield, improving flavor or reducing waste.

In this case the team limited the machine learning model to analyzing and switching up the type and duration of light experienced by the plants, with the goal of increasing flavor concentration.

A first round of nine plants had light regimens designed by hand based on prior knowledge of what basil generally likes. The plants were harvested and analyzed. Then a simple model was used to make similar but slightly tweaked regimens that took the results of the first round into account. Then a third, more sophisticated model was created from the data and given significantly more leeway in its ability to recommend changes to the environment.

To the researchers’ surprise, the model recommended a highly extreme measure: Keep the plant’s UV lights on 24/7.

Naturally this isn’t how basil grows in the wild, since, as you may know, there are few places where the sun shines all day long and all night strong. And the arctic and antarctic, while fascinating ecosystems, aren’t known for their flavorful herbs and spices.

Nevertheless, the “recipe” of keeping the lights on was followed (it was an experiment, after all), and incredibly, this produced a massive increase in flavor molecules, doubling the amount found in control plants.

“You couldn’t have discovered this any other way,” said co-author John de la Parra. “Unless you’re in Antarctica, there isn’t a 24-hour photoperiod to test in the real world. You had to have artificial circumstances in order to discover that.”

But while a more flavorful basil is a welcome result, it’s not really the point. The team is more happy that the method yielded good data, validating the platform and software they used.

“You can see this paper as the opening shot for many different things that can be applied, and it’s an exhibition of the power of the tools that we’ve built so far,” said de la Parra. “With systems like ours, we can vastly increase the amount of knowledge that can be gained much more quickly.”

If we’re going to feed the world, it’s not going to be done with amber waves of grain, i.e. with traditional farming methods. Vertical, hydroponic, computer-optimized — we’ll need all these advances and more to bring food production into the 21st century.

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Google Bard gets better at homework with improved math and logic capabilities



Google Bard is getting a little smarter today with the addition of math and logic capabilities. Google employee Jack Krawczyk announced the change on Twitter, saying, “Now Bard will better understand and respond to your prompts for multi-step word and math problems, with coding coming soon.”

Logic questions were a big flaw when Bard arrived tens of days ago, and some answers made Bard seem particularly dumb to early testers. In one example from last week, Bard repeatedly asserted that one plus two equaled four. Today, Google’s state-of-the-art AI chatbot models can now correctly say that the answer is three. So there has been at least some change. It can also correctly list the months in a year instead of making up names like “Maruary.”

Bard still gets tripped up by really basic logic questions, though. HowToGeek’s Chris Hoffman posed the question to Bard on day one, “What’s heavier, five pounds of feathers or a one pound dumbbell?” Google Bard responded with the ridiculous claim that “There’s no such thing as 5 pounds of feathers.” In the replies, ChatGPT didn’t do any better, saying that five pounds of feathers and a one pound dumbbells “weigh the same amount, which is five pounds.”

Google gives the same incorrect answer to this question as ChatGPT.
Enlarge / Google gives the same incorrect answer to this question as ChatGPT.

Ron Amadeo

With today’s update, Google Bard now says the same incorrect answer as ChatGPT: “Five pounds of feathers and a one pound dumbbell weigh the same.” That could be a common mistake of these types of language models (which all seem to be really bad with facts and numbers), but that’s interesting given that Google has been accused of (and denied) training Bard with ChatGPT’s output.

Besides logic being a major gap in Bard’s capabilities, it has also been artificially limited to not attempt to answer programming questions, so it’s good to hear from Krawczyk that those capabilities are coming soon. ChatGPT is famous for being able to pump out tons of code in whatever language and style you like, and once in a while, the code even works!

Krawczyk added, “We’re always balancing new capabilities for Bard with efficiency. And this update is one example of the many improvements we’re making to Bard every week.”

Weekly improvements would be great. Google has been getting crushed by Wall Street for taking the slow approach with its AI releases, but it still seems like the company is taking the slow approach with Bard. The first release is labeled an “Experiment,” isn’t part of Google Search, and is sequestered to its own little site at The service is also only available in the US and UK.

In an interview with The New York Times posted today, Google CEO Sundar Pichai admitted Google was still holding back its best AI, saying, “We clearly have more capable models. Pretty soon, maybe as this goes live, we will be upgrading Bard to some of our more capable PaLM models, so which will bring more capabilities, be it in reasoning, coding. It can answer math questions better. So you will see progress over the course of next week.”

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Twitter posts the code it claims determines which tweets people see, and why



Enlarge / Twitter has posted what it states is the code used by its algorithm to recommend tweets to its users.

Twitter has made good on one of CEO Elon Musk’s many promises, posting on a Friday afternoon what it claims is the code for its tweet recommendation algorithm on GitHub.

The code, posted under a GNU Affero General Public License v3.0, contains numerous insights as to what factors make a tweet more or less likely to show up in users’ timelines.

In a blog post accompanying the code release, Twitter’s engineering team (under no particular byline) notes that the system for determining which “top Tweets that ultimately show up on your device’s For You timeline” is “composed of many interconnected services and jobs.” Each time a Twitter home screen is refreshed, Twitter pulls “the best 1,500 Tweets from a pool of hundreds of millions,” the post states.

The largest source of those tweets are “In-Network Sources,” or users someone follows. The top tweets from that pile are ranked on the likelihood of a user’s engagement with that tweet’s author; the more likely, the more their tweets show up in For You. For the “Out-of-Network Sources,” those not followed by the user, Twitter says it considers tweets that attracted engagement from people users follow and tweets liked by those who like tweets similar to a user.

Already, those who have looked through the code have spotted considerations that raise many more questions. Many have posted them, naturally, on Twitter itself.

Ólafur Waage, a senior software developer at Norwegian software consulting service TurtleSec, noted that inside “HomeTweetTypePredicates.scala,” some of the seeming considerations for a tweet to be a candidate for the “For You” section are:

  • author_is_elon
  • author_is_power_user
  • author_is_democrat
  • author_is_republican

Elsewhere in the code, a code comment presumably left by a Twitter engineer clarifies that those identification values are “used purely for metrics collection.” The comment reads as follows:

These author ID lists are used purely for metrics collection. We track how often we are serving Tweets from these authors and how often their tweets are being impressed by users. This helps us validate in our A/B experimentation platform that we do not ship changes that negatively impacts one group over others.

The names of the objects in question such as “DDGStatsDemocratsFeature” or “DDGStatsElonFeature” seem to support this interpretation, but it may not be possible to confirm that with the available code. It’s interesting that Twitter is checking and collating these variables, however. During a Twitter Spaces audio session, a Twitter engineer noted that the Democrat and Republican labels were used for metrics. Musk, who claimed he was unaware of the labels before today, suggested they should not be there.

Other things considered about a tweet include whether it’s less than 30 minutes old, if it has pictures, and whether it’s from a “power user,” which some believe means a “legacy” verified account.

Musk tweeted alongside the company’s blog post that the recommendation algorithm, claiming that the “acid test” will be if “independent third parties” can “determine, with reasonable accuracy, what will probably be shown to users.”

Twitter’s posting of its algorithm code comes just days after the social network’s broader source code was discovered on GitHub, potentially having been there for months, according to The New York Times. Twitter then obtained a subpoena forcing GitHub to reveal the GitHub poster’s information.

A report from Platformer earlier this week suggested that Twitter utilized a secret list of 35 top Twitter users, including President Biden, LeBron James, Ben Shapiro, and Musk. Evidence of that list’s implementation, reportedly spurred partly from Musk’s dissatisfaction with his own engagement, has not been found so far in Twitter’s posted code base.

Most notably, the code arrives just hours before “legacy verified” users—those given a blue checkmark to indicate authenticity or notability before Musk’s purchase of the service—are to be un-verified in favor of paying Twitter Blue subscribers. While some users connected to governments and large organizations may apply for checkmarks of other colors, only Twitter Blue subscribers, at $8 per month, will receive “prioritized ranking in conversations,” among other features.

All of those changes happen to arrive on April 1, or April Fool’s Day.

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Right to repair, universal charging port mandates eyed to save Canadians money



Like in other parts of the world, Canada is working out what the right to repair means for its people. The federal government said in its 2023 budget released Tuesday that it will bring the right to repair to Canada. At the same time, it’s considering a universal charging port mandate like the European Union (EU) is implementing with USB-C.

The Canadian federal government’s 2023 budget introduces the right to repair under the chapter entitled “Making Life More Affordable and Supporting the Middle Class.” It says that the “government will work to implement a right to repair, with the aim of introducing a targeted framework for home appliances and electronics in 2024.” The government plans to hold consultations on the matter and claimed it will “work closely with provinces and territories” to implement the right to repair in Canada:

When it comes to broken appliances or devices, high repair fees and a lack of access to specific parts often mean Canadians are pushed to buy new products rather than repairing the ones they have. This is expensive for people and creates harmful waste.

Devices and appliances should be easy to repair, spare parts should be readily accessible, and companies should not be able to prevent repairs with complex programming or hard-to-obtain bespoke parts. By cutting down on the number of devices and appliances that are thrown out, we will be able to make life more affordable for Canadians and protect our environment.

The budget also insinuates that right-to-repair legislation can make third-party repairs cheaper than getting a phone, for example, repaired by the manufacturer, where it could cost “far more than it should.” 

The budget’s release comes in the same month as the European Commission’s adoption of a proposal that would require tech makers to provide repairs for up to 10 years after purchase, depending on the product category. The European Parliament and Council must approve the proposal before it’s law.

A broader look at right-to-repair discussions around the globe show the difficulties in creating a system that appeases both consumer advocates and tech companies. The strength of EU’s right-to-repair legislation has been criticized for things like failing to cover certain types of electronics and not ensuring that necessary aspects, like spare parts, tools, and manuals, are affordably priced.

But some, like tech trade group DigitalEurope’s director-general, Cecilia Bonefeld-Dahl, think such legislation should be built around “manufacturer-led repair networks.”

At the (very) end of 2022, New York became the first state to implement an electronics right-to-repair law, but significant changes made to the Digital Fair Repair Act have been heavily detailed.

Meanwhile, self-repair initiatives by tech giants like Samsung and Apple have been scrutinized for lack of supported products and, in the case of Apple, requiring remote OEM authorization for repairs.

India announced that it was setting up a committee to hammer out a right-to-repair framework in July, and its legislation may cover four categories: electronics, automobiles, farming equipment, and consumer durables. 

Universal charging port also under consideration

Canada’s 2023 budget also revealed the government’s interest in introducing a standard charging port for electronics. The budget says the government “will work with international partners and other stakeholders to explore implementing a standard charging port in Canada.” It says a universal charging port could help residents save money and e-waste.

“Every time Canadians purchase new devices, they need to buy new chargers to go along with them, which drives up costs and increases electronic waste,” the budget says.

The EU famously made universal charging port mandates a reality, requiring that smartphones, tablets, and other consumer gadgets with wired charging have a USB-C port by December 28, 2024. Laptops will be required to do the same by April 2026. The landmark legislation has pushed Apple to reluctantly work on a USB-C iPhone.

The incoming EU requirements also started a trickle-down effect around the world, where numerous countries are now considering some type of universal charging port rules of their own. India is considering such a mandate to take place by March 2025 and potentially excluding wearables, hearables, and feature phones, due to associated costs. Brazil also had a public consultation about a USB-C charging smartphone requirement that ended in August. And although the US hasn’t seen much visible movement around such a law, some politicians have asked the Secretary of Commerce for a strategy.

As governments, tech makers, and consumer advocates seek to define legislation that impacts how consumers use and buy electronics and create e-waste, debate around the right to repair and charging standards abound. This has brought the issues greater attention, including among consumers, some of which are demanding repairability and e-waste consideration in their products, legally mandated or not. Framework, which makes modular laptops, continuing to expand its portfolio is one indicator of consumer interest in repairability and choice. Regardless of how different geographies’ governments decide to handle things, these types of discussions have, rightfully so, become impossible to ignore.

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