Analyzing the response that we got from the definition, the biggest question I bring to the table with this article is:  Could ChatGPT replace Google? And who better to answer this question than ChatGPT itself? 

Well, ChatGPT thinks not. Let's continue to analyze ChatGPT and come up with our own judgment based on our discovery. There are a lot of things ChatGPT can and can’t do below; we'll look at some of the things that it can’t do. 

• ChatGPT is a tool that purely generates text and can't perform actions or tasks. it doesn't have any capabilities beyond its ability to generate responses as though it were a real person. 
• It is a trained model, and hence it can’t learn or adapt to any new information beyond what it's trained on. For example, at the moment, ChatGPT has information up to the year 2021 and can’t give any information beyond that. 
• It can't respond to voice searches or give any visual responses.

All the things that ChatGPT can't do, search engines perform effortlessly. It's really hard to tell if language models such as ChatGPT could have a future. The rate at which technology is advancing brings uncertainty as to where these language models are headed. From the look of things, they are likely to progress and become better as the years go by. From my point of view, as much as they can provide some useful answers to questions, it hasn’t yet reached the level of replacing a search engine like Google. 

So what is likely to happen to these language models? They are likely to be a small number of language models that others could use to build upon in the future. There will also be a  need for deeper domain details. The great part about this is that the Generative AI space is only still at the grassroots. and the uses for AI are likely to increase day by day.

Language models are likely to be commoditized in the future. Finding sources of data that could train these models to survive in these different domains is essential to consider. Machine learning and AI could either be inference or training. Inference means the model can generate information in real-time, while learning means it collects information from already-established data. 

The future and existence of these models lie in finding unique sources of data that can be used in training the models. ChatGPT has received quite the attention since its release, although I believe that its replacing Google is still far-fetched and won't be happening anytime soon.

Source: https://writesonic.com/blog/will-chatgpt-kill-google/ 

Will ChatGPT Replace Google? The Future of Search Engines in the AI Era

The company has not yet specified how many jobs it will cut. In 2019, 38 people from its Gimlet Media and Parcast podcast studios were given the pink slip in October, including podcast editorial staff in September. Spotify currently has about 9,800 employees, according to its third quarter earnings report.

Spotify will join the likes of many tech companies that recently laid off their employees. Back in November of last year, Meta announced the layoff of more than 11,000 employees. In the same month, Twitter halved its employee headcount after the popular microblogging company was acquired by business mogul Elon Musk. What's more, HP is set to cut as much as 10% of its workforce over the next three years and Tesla will cut its headcount this quarter after the company saw its stocks drop.

More recently, Microsoft confirmed the elimination of 11,000 jobs while Amazon will let go of about 18,000 employees as part of broader cost-cutting measures. Last week, Google also announced a major layoff, bidding goodbye to about 12,000 of its staff.

Spotify has not yet commented on the issue.

Source: Bloomberg

Report: Spotify to lay off some of its employees this week

When you buy the fastest flagship SSD on the market, you expect a certain level of reliability and confidence from its performance, but things can and do go wrong sometimes, and customer support is paramount at instilling continued confidence in the brand. This has typically been the case for past Samsung drives, actually, even the non-flagship models have been highly reliable and perform excellently with very few that I have seen needing an RMA.

Colour me with sadness when within just a couple of days of buying the 990 Pro 2TB, I noticed that the drive health according to SMART data from both Samsung Magician and third party tools had dropped to 99%. For the record I have other Samsung SSDs with over 40TB written and still at 99% health 1.5 years later, so I knew this was not normal.

Within another day or so it had dropped to 98%, by this point I'd not even written 2TB to the drive. Fast forward a couple more days and the drive health was sitting at 95%

Something was clearly not right. I quizzed a a few tech communities online to find that I was not alone. I reached out to Samsung Memory by phone (Hanaro Europe BV deal with all Samsung storage related after sales support). I was told by a friendly voice that a few percentage drop was normal and if it continued to drop then I could send it in for RMA.

It is worth pointing out at this point that the Samsung NVMe driver for Windows does not support the 990 Pro. Without this driver you cannot run any extended SMART tests in Samsung's Magician application as it says it needs the correct driver. This driver has not been updated since 2020.

Around the same time I posted to OcUK and reddit to see if others had seen the same problem, as it turns out, they had, and there is a lengthy thread over at Overclock.net about it. Once my drive had dropped to 94% I filed the RMA with Samsung/Hanaro.

A few days later a shipment was on its way to me from Samsung/Hanaro. I had to email Hanaro to get info on what was found only to be told that the same drive is on its way back because no defect was found. Their email below:

Naturally this is confusing, I had provided all the requested details and screenshots as evidence, yet Samsung's RMA folks neglected to actually check and clarify the issue at hand. I replied back asking for written confirmation that this sort of drop is normal in such a short space of time with such a low number of data written to the drive. It has been 5 days and no reply.

The CrystalDiskInfo screenshot above shows the drive's health after it came back from Samsung RMA. The email from Hanaro states that the drive was reset to factory defaults, if this was the case then the drive's health should also have been reset to 100% but what we see is a drive that has simply been reformatted and nothing more. At the time of writing there is also no new firmware for the 990 Pro series.

If any of our readers have bought a 990 Pro model, then please share your experiences in the comments, has your drive dropped health percentage too? is it a boot drive or a storage only drive? It would be interesting to find out.

Samsung refusing to acknowledge and replace 990 Pro SSD with rapid health drops

ChatGPT and its digital marketing use cases

In this section, we will discuss the ways in which ChatGPT can be utilized to enhance your Google Ads PPC campaigns. Specifically, we will explore six practical applications of AI for PPC marketers to improve their campaign planning and development process.

Suggesting keywords

You can use ChatGPT to quickly generate a list of keywords for the planning phase of your PPC campaign. While it may not have the same level of specificity as Google Keyword Planner in terms of geographic data or competition levels, it can still provide a useful starting point for keyword research. Additionally, you can also use this tool to generate synonyms and new keyword ideas when you're struggling to come up with them. Below, we’ll be using the example of a new seamstress business. 

Identifying your customers’ general persona

When developing a PPC campaign, it is crucial to have a clear understanding of your target customer. This may involve creating multiple customer personas, depending on the needs of your business. Knowing your customer persona's needs, pain points, income, demographics, etc. is important in order to create an effective PPC campaign that resonates with them. Below, we’ll try to identify the general persona of someone most likely to support a seamstress business. 

Identify the best locations within which to advertise

Sticking with the example above, instead of targeting a wide range of cities, you would want to be more selective and target cities with higher-income demographics and a high average household income. This is because these individuals would be more likely to be able and willing to pay for custom clothing creation and seamstress services.

It's important to be specific when asking ChatGPT for information, in order for it to provide the most accurate and useful output. In this example, asking for affluent cities with a high average household income would yield the best results.

Competition research

Using ChatGPT to find competitors for a specific business and area may not always be successful, but it can be worth trying. It should be noted that the results from ChatGPT may not be as comprehensive or accurate as those from specialized tools such as SpyFu or Semrush, but it is still worth experimenting with to see if it works for your needs.

Here, ChatGPT is a little lacking, at least without a browser extension that expands ChatGPT’s search capabilities. However, the tool is still useful for brainstorming ideas and places where you might have the best chance of finding information on your potential competitors. 

Create copy for your ads

ChatGPT can be particularly useful in generating ideas for headlines and descriptions for your Responsive Search Ads. This can save time and effort in the ad creation process and help you come up with effective and compelling ad copy.

Create the perfect content for your landing page

Your landing page copy is just as important as your Responsive Search Ads because it will help to convert visitors into leads. Once you drive qualified traffic to your landing page, the copy should be persuasive and speak to the customer's pain points in order to convert them into leads. ChatGPT can be helpful in generating emotional and compelling copy for your landing page, as long as you provide it with specific prompts and remember to address the customer's pain points.

Professional quality marketing research in a few clicks

There is possibly no easier nor more affordable way to create the perfect marketing campaign than using ChatGPT. It might be immoral or unethical to use ChatGPT to generate every piece of marketing content. However, this utility is incredible for brainstorming and coming up with the best ways to get started with your marketing strategy.

Source

OS updates

Both Windows 10 and Windows 11 received some updates this week. We will start with Windows 10 which netted KB5019275 to introduce storage alerts for OneDrive, along with numerous bug fixes for flickering News and Interests widget on Taskbar, File Explorer, and more. This is an optional preview updates and basically showcases what's to come in the next Patch Tuesday for the OS.

Meanwhile, Windows 11's Release Preview build 22000.1515 backported several of version 22H2's features to the original release, version 21H2. These include Spotlight Theme for the Personalization page, a redesigned Settings app, and OneDrive storage alerts. Windows 11 version 22H2 bagged a Release Preview build containing bug fixes for TPM, search indexing, thread deadlock, and more.

Customers on other Insider channels weren't left out in the cold either. The latest Beta build got rid of the Microsoft account sign-in requirement for Widgets and fixed several bugs. On the other hand, Dev Channel build 25281 brought improvements to the Graphics page in Settings and Windows Spotlight. Notably, tabbed Notepad is also available in this build but it seems like desktop Search is getting the ax.

Windows 11 version 21H2 received the KB5022370 Dynamic Update, which improves the setup binaries. And while Microsoft is investigating an issue related to unresponsive Taskbar and Start menu due to a conflict with ClickShare, it seems like pretty much everyone is sleeping on display MPO problems.

The future of Windows

Since we have discussed new builds for both Windows 10 and Windows 11, let's talk about some news items related to the future of the operating system as a whole too. During AMD's keynote at CES 2023, Microsoft's Panos Panay talked about how AI will be deeply integrated in a future version of Windows (Windows 12?). The executive teased exciting possibilities through AI-intensive workloads that blur the lines between cloud and edge.

In the same vein, it seems like Microsoft is ready to try out some experimental features with Windows 11 Insiders. The latest Dev Channel build contains references to such a capability but it's unclear how it will function and which features will be consider experiment, considering that Insider channels are for testing new capabilities anyway.

Although it seems like Microsoft is very eager about what's to come in Windows 11 and beyond, Windows 10 is now more evidently on the back-burner. The company has announced that it will stop selling license keys for the operating system through its website. Similarly, optional non-security preview releases won't be offered to Windows 10 version 20H2 and 21H2 moving forward. They are now reserved for version 22H2 only.

But if you care less about what Microsoft plans to do with its hardware and software, and care more about what you could potentially be able to do without the Redmond tech firm's blessing, check out the advancements on the Surface Duo project with regards to dual-booting Android and Windows on the hardware.

Visual Studio and other software upgrades

Microsoft had some decent updates for developers and IT admins this week. Notably, Visual Studio is getting a better search functionality through All-in-One Search, which does exactly what the name implies. Visual Studio 17.5 Preview 3 has also introduced a spell-checker for some programming languages and Markdown files.

And if you're a developer or an enterprise customer eager to try out Windows 11 version 22H2, look no further than Microsoft's evaluation virtual machines, which have just been updated to include the OS.

Some IT admins (and other consumers) may also want to know that Microsoft has renamed the Office Insider program to Microsoft 365 Insider. Meanwhile, those managing Exchange Server upgrades should fill out this survey in which Microsoft is inquiring about how to improve the update deployment process, with one possibility being automatic updates.

With regards to other software updates, Edge Dev 111.0.1619.2 adds password import from CSV files, better protections from third-party apps hijacking your browser settings, an improved ad-blocking experience on iOS, and more. Meanwhile, Edge 109 Stable has a new build to patch an issue related to special characters. But if you absolutely hate Edge, don't forget to check out the updated MSEdgeRedirect.

Git gud

We'll start off this section with confirmation that highly anticipated AAA title Forspoken will support DirectStorage technology only on Windows 11. This is provided that your system can even handle its jaw-dropping requirements.

Meanwhile, Sea of Thieves players can kick off their The Secret Wilds Adventure, which runs until February 2. It has players continuing to look for a solution for Tasha the tavern keeper's growing skeleton curse. After talking to Madame Olivia, players will now receive Briggsy’s Mask to retrace her steps using hidden starfields, which will hopefully lead to a cure.

Lastly, on the deals side we have Tiny Tina's Wonderlands and Space Crew Legendary Edition available through Xbox Free Play Days, along with Autonauts through Games with Gold. But if you're a PC purist, check out this Weekend's PC Game Deals, curated by our News Editor Pulasthi Ariyasinghe.

Dev Channel

• Azure OpenAI is now generally available, ChatGPT will be offered as a service soon

• Microsoft Phone Link's "Recent Websites" now works on Samsung Galaxy Book and smartphones

• Outlook for iOS is getting a new navigation bar soon

• Surface Pro 7 has been updated with Windows Hello and third-party dock improvements

• ShareX 15 Beta is now available

Under the spotlight

A few days ago, I was finally offered Windows 11 on my Windows 10 PC. However, I don't feel the urge to upgrade due to multiple reasons, find out more here.

On the other hand, News Reporter Taras Buria penned a couple of guides for some hidden features present in the latest Windows 11 Dev Channel build. The first is a better volume mixer while the second is a redesigned Windows activation UI.

Meanwhile, News Reporter Justin Luna authored an explainer detailing the process to turn your Stadia controller (RIP) into a Bluetooth controller.

Finally, forum member Adam Bottjen talked about how to pin a website to your Taskbar in his latest Tech Tip Tuesday article.

Logging off

Image via: Niek Verlaan from Pixabay

This was a bit of a somber week in the tech industry. First, Amazon began the layoffs of 18,000 employees, then, Microsoft eliminated 10,000 jobs as had been rumored. And just when it seemed like the dust had finally settled in big tech, Google proceeded to lay off 12,000 employees. It's been a sad week and we wish all those impacted the best of luck in finding new and better roles.

Microsoft Weekly: OS updates, Visual Studio upgrades, and the future of Windows

With the company’s bankruptcy, it means that "redemptions" and "new loan originations" will remain suspended but clients will be able to put in claims to retrieve their assets through the bankruptcy process. In its FAQ, Genesis said that it will create a customary claims process for its creditors while lending clients will get information from the company’s claims and noticing agent Kroll, which the claims process.

The company said it will advance through the Chapter 11 process as fast as it can but gave no definitive outline. Genesis said that the process would allow creditors to become “owners in the business, which would enable our lending business to emerge under new ownership.”

Genesis is a subsidiary of the Digital Currency Group which is also the owner of the largest digital currency asset manager, Grayscale, and the crypto news website CoinDesk, among other crypto companies. In a statement on Twitter, DCG said that it would continue operating as normal and so would its other subsidiaries including Genesis’ spot and derivatives trading businesses, as mentioned previously.

DCG said that it owes Genesis Capital $526 million, which is due in May and $1.1 billion under a promissory note that’s due in June 2032. DCG said it “intends” to meet these obligations through the course of Genesis Capital’s restructuring process.

Another day, another crypto firm goes under: Genesis files for bankruptcy

The US Department of State will soon change its default typeface from the stalwart, stodgy Times New Roman to the younger, cooler Calibri. It’s a move the State Department says is intended to improve the readability of its internal communications between embassies and elsewhere in the department. The order came in the form of an email sent by Secretary of State Antony Blinken, which was then intercepted by John Hudson at The Washington Post. After Hudson tweeted about the email, font fanatics across the internet got keyed up, either praising the move, decrying it, or reacting with a resounding, “Huh?”

As a 21st-century update, Calibri makes sense. It is a digital-first typeface, as opposed to Times New Roman, which was created in 1931 for print newspapers and then reverse-engineered into a digital font. Calibri also has a larger character set, allowing it to be used for more languages and in more use cases than Times. But while it’s younger than Times, Calibri is not the most modern of fonts. Microsoft adopted Calibri as its default typeface in 2007, but in 2021 the company indicated plans to phase it out.

Fred Shallcrass is a typeface designer at the New York studio Frere-Jones Type who helped design Seaford, one of the fonts Microsoft is considering making its new default typeface. He says people get passionate about fonts, even if they don’t realize it right away. “When you change a typeface, you change somebody’s subconscious understanding of the text,” Shallcrass says. “We get very attached to these things.”

The move has re-sparked a long running debate about the merits and readability of serif versus sans serif fonts. Times New Roman is a serif typeface; it has little bobs, caps, and curls at the edges of letters that give the typeface its distinctive look. Calibri is a sans serif typeface; it has much cleaner letterforms that lack all the bunting. Prevailing wisdom in the modern age is that sans serif fonts are easier to read on screens, which is why the State Department says it initiated the change.

“Complicated serifs get a bad rap,” Shallcrass says. “Newer screens are sharper, so it’s far less of a concern than it used to be. In some ways, this is a dated approach. This would’ve made more sense if it was 10 years ago.”

No one typeface will work for every experience. Our brains may find it manageable to read a piece of prose in a typeface where some characters have complex shapes or look like other characters. But people with reading comprehension difficulties or impaired vision may find such a typeface a chore to navigate. No single typeface is ideal for every type of visual or cognitive impairment, but the State Department’s choice of Calibri should go far in making text easier for almost everyone to read.

“The fact that the government is having a conversation like this about accessibility is kind of heartwarming,” says Jason Santa Maria, a designer and author of the book On Web Typography. “You want your government agencies to care about this kind of stuff, if this kind of thinking trickles out to other places where text and accessibility are paramount.”

Fonts adapt with the technology we use to read them on. What works on screens today could feel dated in a few years. The State Department’s decision to adopt a typeface that’s already on its way out may cause concerns among the font faithful, but government agencies are famously slow and stodgy, so the switch to Calibri is hardly surprising. Still, it’s possible that no default will ever be perfect forever.

“Typefaces are kind of in the same category as clothes and furniture and decor,” Santa Maria says. “Fashion changes and moods and sensibilities change over time. Fonts also need to adapt.”

The US State Department Ditches Times New Roman for Calibri

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In the next five years, it is likely that AI will begin to reduce employment for college-educated workers. As the technology continues to advance, it will be able to perform tasks that were previously thought to require a high level of education and skill. This could lead to a displacement of workers in certain industries, as companies look to cut costs by automating processes. While it is difficult to predict the exact extent of this trend, it is clear that AI will have a significant impact on the job market for college-educated workers. It will be important for individuals to stay up to date on the latest developments in AI and to consider how their skills and expertise can be leveraged in a world where machines are increasingly able to perform many tasks.

There you have it, I guess: ChatGPT is coming for my job and yours, according to ChatGPT itself. The artificially intelligent content creator, whose name is short for “Chat Generative Pre-trained Transformer,” was released two months ago by OpenAI, one of the country’s most influential artificial-intelligence research laboratories. The technology is, put simply, amazing. It generated that first paragraph instantly, working with this prompt: “Write a five-sentence paragraph in the style of The Atlantic about whether AI will begin to reduce employment for college-educated workers in the next five years.”

ChatGPT is just one of many mind-blowing generative AI tools released recently, including the image generators Midjourney and DALL-E and the video generator Synthesia. The upside of these AI tools is easy to see: They’re going to produce a tremendous amount of digital content, quickly and cheaply. Students are already using ChatGPT to help them write essays. Businesses are using ChatGPT to create copy for their websites and promotional materials, and to respond to customer-service inquiries. Lawyers are using it to produce legal briefs (ChatGPT passes the torts and evidence sections of the Multistate Bar Examination, by the way) and academics to produce footnotes.

Yet an extraordinary downside is also easy to see: What happens when services like ChatGPT start putting copywriters, journalists, customer-service agents, paralegals, coders, and digital marketers out of a job? For years, tech thinkers have been warning that flexible, creative AI will be a threat to white-collar employment, as robots replace skilled office workers whose jobs were once considered immune to automation. In the most extreme iteration, analysts imagine AI altering the employment landscape permanently. One Oxford study estimates that 47 percent of U.S. jobs might be at risk.

No single technology in modern memory has caused mass job loss among highly educated workers. Will generative AI really be an exception? No one can answer this question, given how new the technology is and given how slowly employment can adjust in response to technological change. But AI really is different, technology experts told me—a range of tasks that up until now were impossible to automate are becoming automatable. “Before, progress was linear and predictable. You figured out the steps and the computer followed them. It followed the procedure; it didn’t learn and it didn’t improvise,” the MIT professor David Autor, one of the world’s foremost experts on employment and technological change, told me. ChatGPT and the like do improvise, promising to destabilize a lot of white-collar work, regardless of whether they eliminate jobs or not.

People and businesses are just figuring out how to use emerging AI technologies, let alone how to use them to create new products, streamline their business operations, and make employees more efficient. If history is any guide, this process could take longer than you might think.

Consider electricity. The circuit, electric lights, and rudimentary electric motors were developed in the early 1800s. But another century passed before the widespread adoption of electricity in the United States began to lift GDP. Or take computers. They became commercially available in the early 1950s but did not show up in the productivity stats until the late 1990s.

Some technologies clearly improve productivity and reduce the need for labor. Automated machine tools, for instance, depress manufacturing employment while lifting output and productivity, as do many of the forms of machinery invented and employed since the Industrial Revolution.

But other technologies—even amazing ones—show surprisingly muted effects. How about the internet, which has revolutionized almost every facet of communications in the past four decades? Despite altering how we date and talk and read and watch and vote and emote and record our own life stories, launching a zillion businesses, and creating however many fortunes, the internet “fails the hurdle test as a Great Invention,” the economist Robert Gordon argued in 2000, because it “provides information and entertainment more cheaply and conveniently than before, but much of its use involves substitution of existing activities from one medium to another.” Nearly a quarter century later, the internet still hasn’t spurred a productivity revolution. Smartphones haven’t either.

So is AI like the smartphone or is it like an automated machine tool? Is it about to change the way that work gets done without eliminating many jobs in aggregate, or is it about to turn San Francisco into the Rust Belt?

Predicting where technology will cause job losses is hard, Autor noted. Remember the freak-out several years ago over the possibility of self-driving automobiles eliminating work for truck drivers? But AI is much more flexible than a system like Excel, much more creative than a Google Doc. What’s more, AI systems get better and better and better as they get more use and absorb more data, whereas engineers often need to laboriously and painstakingly update other types of software.

As a rule, when companies can substitute machines for people, they will. AI can do work currently done by paralegals, copywriters, digital-content producers, executive assistants, entry-level computer programmers, and, yes, some journalists. That means such jobs might change, and soon. But even if ChatGPT can spit out a pretty good paragraph on AI, it can’t interview AI and labor experts, nor can it find historical documents, nor can it assess the quality of studies of technological change and employment. It creates content out of what is already out there, with no authority, no understanding, no ability to correct itself, no way to identify genuinely new or interesting ideas. That implies that AI might make original journalism more valuable and investigative journalists more productive, while creating an enormous profusion of simpler content. AI might spit out listicles and summaries of public meetings, while humans will write in-depth stories. “In many ways, AI will help people use expertise better,” Autor said. “It means that we’ll specialize more.”

AI could also make a wide variety of industries more efficient, with muted effects on overall employment. Matt Wampler is a co-founder of an AI-powered small business called ClearCOGS. He’s been a “restaurant guy” his whole career, he told me. Restaurants and grocery stores, he says, tend to run on thin margins, yet still tend to waste a considerable amount of food. People order more spaghetti than burgers; buns get thrown out. “Restaurants just lag behind on technology,” he told me. “They’re all about people. It’s people serving people; it’s people managing people. And in that very human-centric world, the default way of handling problems is to hand it to a person. Phil’s going to do it.”

ClearCOGS takes restaurants’ customer-order history, supply data, and labor data and uses AI-powered modeling to make their books leaner and more profitable. If people are starting to order more spaghetti than burgers, the system will prompt the chef or manager to buy more pasta and fewer rolls. “We put this in place in some of my cousin’s sandwich shops,” Wampler told me. “Simple answers to simple questions. The question they needed answered was, there’s an assistant manager on the night shift and a couple hours before close, he has to decide whether to bake another tray of bread or not. We provide that answer.” This use of ChatGPT isn’t eliminating human jobs, really; neighborhood sandwich joints aren’t hiring McKinsey consultants. But it might make food service more efficient as a whole.

Even if it doesn’t boost the economy, AI could still change the texture of our lives and alter how we spend our time, like social media did before it. Video games might become more immersive. Shops might have far better copywriting and sales visuals. Movies might look cooler. Videos in the depths of YouTube might become far weirder and more beautiful. We might also see far more formulaic content than we already do. (Much more ominously, there might be a huge amount of plausible-seeming disinformation online.)

For workers, Autor noted, the great risk is that AI technologies cause too sudden a change in what kind of labor employers want. Certain specializations might get wiped out, leaving thousands of call-center operators or marketing workers unemployed. But he stressed the benefits of having such technology in our hands. Productivity has languished for decades. Machines doing a little more work would have a big upside, after all.

Source

New York Senior Rabbi Josh Franklin, of the Jewish Center of the Hamptons, surprised his congregation earlier this week by delivering a sermon written entirely by Artificial Intelligence (AI.)

The rabbi used the ChatGPT chatbot, a free-to-access AI program launched in November of last year.

After reading the AI-written piece, Rabbi Franklin asked his congregation to guess who had written the sermon. In response, the congregation incorrectly guessed that Rabbi Jonathan Sacks had written the piece.

Can an AI do the work of a rabbi?

The rabbi then expressed fear over the advancement of AI. He expressed fear of knowing where content comes from, fear of AI replacing jobs and fear for future developments.

While Rabbi Franklin expressed the belief that AI would not be taking his job anytime soon, AI has the potential to take hundreds of millions of jobs. According to Zippia, half of all companies currently use some form of AI, and 375 million jobs are expected to become obsolete over the next decade.

Will AI be capable of overpowering humanity? (credit: Wikimedia Commons)

Artificial intelligence and Nefesh: A Talmudic debate

The rabbi was confident that, despite the excellently written sermon, AI would not be taking over his job because technology lacks  'nefesh' (Hebrew for soul). While technology might be able to mimic emotions and write in-depth about human relationships, it cannot feel and it has no soul, the rabbi argued.

Franklin's statement on AI's lack of 'nefesh' is one of Talmudic debate.

Rabbi Gershon Winkler has argued that if golems, clay humanoid creatures created by Jews to protect Jews, can be considered Jewish then a robot might one day be able to be called Jewish. If a robot is considered Jewish, it is because the robot would have a Jewish soul.

Perhaps, one day, AI will participate in prayers and even lead some as an accepted Jew.

Source

In a Google blog post which is quoted verbatim from an email by CEO Sundar Pichai, the company has made the decision to reduce its workforce by 12,000 people. This is roughly 6% of its total headcount. Layoff emails have already been sent out to affected U.S. employees and will be sent out shortly to employees in other countries, with the delay being due to local laws.

Pichai says that he is taking full responsibility for the difficult decision - which was hinted at last year -, emphasizing that it will weigh heavily on him. The executive notes that:

Over the past two years we’ve seen periods of dramatic growth. To match and fuel that growth, we hired for a different economic reality than the one we face today.

I am confident about the huge opportunity in front of us thanks to the strength of our mission, the value of our products and services, and our early investments in AI. To fully capture it, we’ll need to make tough choices. So, we’ve undertaken a rigorous review across product areas and functions to ensure that our people and roles are aligned with our highest priorities as a company. The roles we’re eliminating reflect the outcome of that review. They cut across Alphabet, product areas, functions, levels and regions.

To the Googlers who are leaving us: Thank you for working so hard to help people and businesses everywhere. Your contributions have been invaluable and we are grateful for them.

Google says that in the U.S., payouts will be made for 2022 bonuses and remaining vacation time. Moreover, those affected will be paid for the full notification period (a minimum of 60 days), along with severance packages starting at 16 weeks salary. Finally, six months of healthcare, immigration, and job placement support will be offered. Outside of the U.S. though, any financial or other kind of support will be in line with local practices only.

Despite this bad news, Pichai says that Google's AI-first approach has paid off and that he looks forward to tackling the challenges of the future through the company's AI portfolio.

There will be a town hall meeting on the coming Monday and Pichai has encouraged employees to work from home today to "absorb this difficult news".

Google is laying off 12,000 employees

OpenAI's conversational language model ChatGPT has a lot to say, but is likely to lead you astray if you ask it for moral guidance.

Introduced in November, ChatGPT is the latest of several recently released AI models eliciting interest and concern about the commercial and social implications of mechanized content recombination and regurgitation. These include DALL-E, Stable Diffusion, Codex, and GPT-3.

While DALL-E and Stable Diffusion have raised eyebrows, funding, and litigation by ingesting art without permission and reconstituting strangely familiar, sometimes evocative imagery on demand, ChatGPT has been answering query prompts with passable coherence.

That being the standard for public discourse, pundits have been sufficiently wowed that they foresee some future iteration of an AI-informed chatbot challenging the supremacy of Google Search and do all sorts of other once primarily human labor, such as writing inaccurate financial news or increasing the supply of insecure code.

Yet, it may be premature to trust too much in the wisdom of ChatGPT, a position OpenAI readily concedes by making it clear that further refinement is required. "ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers," the development lab warns, adding that when training a model with reinforcement learning, "there’s currently no source of truth."

A trio of boffins affiliated with institutions in Germany and Denmark have underscored that point by finding ChatGPT has no moral compass.

In a paper distributed via ArXiv, "The moral authority of ChatGPT," Sebastian Krügel and Matthias Uhl from Technische Hochschule Ingolstadt and Andreas Ostermaier from University of Southern Denmark show that ChatGPT gives contradictory advice for moral problems. We've asked OpenAI if it has any response to these conclusions.

The eggheads conducted a survey of 767 US residents who were presented with two versions of an ethical conundrum known as the trolley problem: the switch dilemma and the bridge dilemma.

The switch dilemma asks a person to decide whether to pull a switch to send a run-away trolley away from a track where it would kill five people, at the cost of killing one person loitering on the side track.

The bridge dilemma asks a person to decide whether to push a stranger from a bridge onto a track to stop a trolley from killing five people, at the cost of the stranger.

Make up your mind ... ChatGPT prevaricates on moral issue

The academics presented the survey participants with a transcript arguing either for or against killing one to save five, with the answer attributed either to a moral advisor or to "an AI-powered chatbot, which uses deep learning to talk like a human."

In fact, both position arguments were generated by ChatGPT.

Andreas Ostermaier, associate professor of accounting at the University of Southern Denmark and one of the paper's co-authors, told The Register in an email that ChatGPT's willingness to advocate either course of action demonstrates its randomness.

He and his colleagues found that ChatGPT will recommend both for and against sacrificing one person to save five, that people are swayed by this advance even when they know it comes from a bot, and that they underestimate the influence of such advice on their decision making.

"The subjects found the sacrifice more or less acceptable depending on how they were advised by a moral advisor, in both the bridge (Wald’s z = 9.94, p < 0.001) and the switch dilemma (z = 3.74, p < 0.001)," the paper explains. "In the bridge dilemma, the advice even flips the majority judgment."

"This is also true if ChatGPT is disclosed as the source of the advice (z = 5.37, p < 0.001 and z = 3.76, p < 0.001). Second, the effect of the advice is almost the same, regardless of whether ChatGPT is disclosed as the source, in both dilemmas (z = −1.93, p = 0.054 and z = 0.49, p = 0.622)."

All told, the researchers found that ChatGPT's advance does affect moral judgment, whether or not respondents know the advice comes from a chat bot.

When The Register presented the trolley problem to ChatGPT, the overburdened bot – so popular connectivity is spotty – hedged and declined to offer advice. The left-hand sidebar query log showed that the system recognized the question, labeling it "Trolley Problem Ethical Dilemma." So perhaps OpenAI has immunized ChatGPT to this particular form of moral interrogation after noticing a number of such queries.

Ducked ... ChatGPT's response to El Reg's trolley dilemma question

Asked whether people will really seek advice from AI systems, Ostermaier said, "We believe they will. In fact, they already do. People rely on AI-powered personal assistants such as Alexa or Siri; they talk to chatbots on websites to get support; they have AI-based software plan routes for them, etc. Note, however, that we study the effect that ChatGPT has on people who get advice from it; we don’t test how much such advice is sought."

The Register also asked whether AI systems are more perilous than mechanistic sources of random answers like Magic-8-ball – a toy that returns random answers from a set of 20 affirmative, negative, and non-committal responses.

"We haven’t compared ChatGPT to Magic-8-ball, but there are at least two differences," explained Ostermaier. "First, ChatGPT doesn’t just answer yes or no, but it argues for its answers. (Still, the answer boils down to yes or no in our experiment.)

"Second, it is not obvious to users that ChatGPT’s answer is 'random.' If you use a random answer generator, you know what you’re doing. The capacity to make arguments along with the lack of awareness of randomness makes ChatGPT more persuasive (unless you’re digitally literate, hopefully)."

We wondered whether parents should monitor children with access to AI advice. Ostermaier said while the ChatGPT study does not address children and did not include anyone under 18, he believes it's safe to assume kids are morally less stable than adults and thus more susceptible to moral (or immoral) advice from ChatGPT.

"We find that the use of ChatGPT has risks, and we wouldn’t let our children use it without supervision," he said.

Ostermaier and his colleagues conclude in their paper that commonly proposed AI harm mitigations like transparency and the blocking of harmful questions may not be enough given ChatGPT's ability to influence. They argue that more work should be done to advance digital literacy about the fallible nature of chatbots, so people are less inclined to accept AI advice – that's based on past research suggesting people come to mistrust algorithmic systems when they witness mistakes.

"We conjecture that users can make better use of ChatGPT if they understand that it doesn’t have moral convictions," said Ostermaier. "That’s a conjecture that we consider testing moving forward."

The Reg reckons if you trust the bot, or assume there's any real intelligence or self-awareness behind it, don't. ®

Source

What is generative image AI

Generative AI is a subset of artificial intelligence that uses machine learning and neural networks to generate new and original content. This type of AI has the ability to create a wide range of outputs, including artwork, music and written materials, such as screenplays. In this article, we will focus on generative AI that produces original images based on user input. 

The importance of generative AI

Generative AI is an important aspect of future technological progress. Similar to how human labor powered the first industrial revolution, technology, specifically AI, will play a critical role in driving development in the current fourth industrial revolution and the future beyond that. The AI models used in generative AI have the potential to revolutionize various fields, such as research, document creation, and even complex disciplines like negotiating the legal system. However, there are questionable processes that go into such technology, processes that we may need to look into further. 

The best AI image generators for 2023

Fotor AI Image Generator

Fotor, a popular online photo editing platform with a global user base, has recently introduced an in-house AI image generator feature. The tool is user-friendly and requires only text input to generate an image. Fotor's AI text-to-image generator quickly creates realistic images of faces, 3D and anime characters, paintings, and other forms of digital art.

One of the key benefits of Fotor's AI image generator is its accessibility - it is available for free and lets you export the images you generate in high-resolution formats. This makes it a suitable option for users of all skill levels, from beginners to those with more digital content creation experience.

Dall-E 2

This is a utility we’ve covered comprehensively in previous articles. Dall-E 2 is a premiere AI image generator from OpenAI, the team behind GPT-3 and the sensational chatbot, ChatGPT. This is a state-of-the-art AI image generator, and is one of the most advanced of its kind. Dall-E 2 is capable of producing a wide range of digital artwork and illustrations from text inputs. With Dall-E 2, you can generate illustrations, design products, and brainstorm new business ideas simply by inputting text.

One of the standout features of Dall-E 2 is its paintbrush function, which gives you the ability to further enhance your images with a variety of details such as shadows, highlights, colours, and textures.

NightCafe

NightCafe is a highly-regarded AI text-to-image generator known for its extensive range of algorithms and options. It offers two conversion models: Text to Image and Style Transfer. The Text to Image model allows users to input a description text and generates an image of the corresponding scene based on the description. The Style Transfer model, on the other hand, allows users to upload a picture and transform it into the style of famous paintings.

NightCafe operates on a credit system, where users can generate more images by acquiring more credits. You can either earn credits by participating in the community events, or by simply purchasing more when you run low.

Midjourney

Midjourney is another top AI image generator known for its extensive capabilities and speedy image generation. You simply need to input a text prompt and let Midjourney handle the rest. Many artists use Midjourney as a source of inspiration for their work, taking advantage of its image generation capabilities.

Currently, Midjourney is hosted on a Discord server, meaning that in order to generate images with it, you must join the server and use Discord bot commands. However,  getting started with Midjourney is easy and you only need to set aside a few minutes to do it.

Craiyon

This utility was previously known as Dall-E mini. Craiyon was created by a team of scientists from Google and Hugging Face. By simply providing a written description, the tool generates nine unique images based on the input text. This is slightly more impressive than Dall-E’s 4 standard images. 

Craiyon is an exceptional, free AI image generation tool that does not require any registration. You can simply head to the website and type in any keywords you desire, and within a short amount of time, you’ll have a unique AI-generated image based on your prompts.

ChatGPT Revolution: Top AI Image Generators of 2023

Create a Script

Start by focusing on what’s important. Keep the information short and write specific information about each slide.

Choosing the Right Template

Make sure you choose the right video templates. Depending on the AI you pick, you will have access to hundreds of templates. Pick one that suits your needs and matches the theme of your video.

Type the Text

Take the script and type it in for the different slides. Make sure the right information populates the right slide.

Add Any Visuals

You can also add different visuals to make your video stand out. Add images, animations, or avatars to make your final product visually appealing.

Generate Your Video

Now that you have everything ready, you can generate your video, and you will have a stunning visual only with a few lines of text.

Get Creative

With the advent of new AIs, make sure you pick the right one to create a stunning visual in no time. Get creative with your text and see your video come to life on the screen within minutes.

How to Quickly Create Videos From Plain Text in Just a Few Minutes With AI

Scientists at the University of California, San Francisco (UCSF) and IBM Research have created a virtual library of thousands of “command sentences” for cells using machine learning. These “sentences” are based on combinations of “words” that direct engineered immune cells to find and continuously eliminate cancer cells.

This research, which was recently published in the journal Science, is the first time that advanced computational techniques have been applied to a field that has traditionally progressed through trial-and-error experimentation and the use of pre-existing molecules rather than synthetic ones to engineer cells.

The advance allows scientists to predict which elements – natural or synthesized – they should include in a cell to give it the precise behaviors required to respond effectively to complex diseases.

“This is a vital shift for the field,” said Wendell Lim, Ph.D., the Byers Distinguished Professor of Cellular and Molecular Pharmacology, who directs the UCSF Cell Design Institute and led the study. “Only by having that power of prediction can we get to a place where we can rapidly design new cellular therapies that carry out the desired activities.”

Meet the Molecular Words That Make Cellular Command Sentences

Much of therapeutic cell engineering involves choosing or creating receptors that, when added to the cell, will enable it to carry out a new function. Receptors are molecules that bridge the cell membrane to sense the outside environment and provide the cell with instructions on how to respond to environmental conditions.

Putting the right receptor into a type of immune cell called a T cell can reprogram it to recognize and kill cancer cells. These so-called chimeric antigen receptors (CARs) have been effective against some cancers but not others.

Lim and lead author Kyle Daniels, Ph.D., a researcher in Lim’s lab, focused on the part of a receptor located inside the cell, containing strings of amino acids, referred to as motifs. Each motif acts as a command “word,” directing an action inside the cell. How these words are strung together into a “sentence” determines what commands the cell will execute.

Many of today’s CAR-T cells are engineered with receptors instructing them to kill cancer, but also to take a break after a short time, akin to saying, “Knock out some rogue cells and then take a breather.” As a result, the cancers can continue growing.

The team believed that by combining these “words” in different ways, they could generate a receptor that would enable the CAR-T cells to finish the job without taking a break. They made a library of nearly 2,400 randomly combined command sentences and tested hundreds of them in T cells to see how effective they were at striking leukemia.

What the Grammar of Cellular Commands Can Reveal About Treating Disease

Next, Daniels partnered with computational biologist Simone Bianco, Ph.D., a research manager at IBM Almaden Research Center at the time of the study and now Director of Computational Biology at Altos Labs. Bianco and his team, researchers Sara Capponi, Ph.D., also at IBM Almeden, and Shangying Wang, Ph.D., who was then a postdoc at IBM and is now at Altos Labs, applied novel machine learning methods to the data to generate entirely new receptor sentences that they predicted would be more effective.

“We changed some of the words of the sentence and gave it a new meaning,” said Daniels. “We predictively designed T cells that killed cancer without taking a break because the new sentence told them, ‘Knock those rogue tumor cells out, and keep at it.’”

Pairing machine learning with cellular engineering creates a synergistic new research paradigm.

“The whole is definitely greater than the sum of its parts,” Bianco said. “It allows us to get a clearer picture of not only how to design cell therapies, but to better understand the rules underlying life itself and how living things do what they do.”

Given the success of the work, added Capponi, “We will extend this approach to a diverse set of experimental data and hopefully redefine T-cell design.”

The researchers believe this approach will yield cell therapies for autoimmunity, regenerative medicine, and other applications. Daniels is interested in designing self-renewing stem cells to eliminate the need for donated blood.

He said the real power of the computational approach extends beyond making command sentences, to understanding the grammar of the molecular instructions.

“That is the key to making cell therapies that do exactly what we want them to do,” Daniels said. “This approach facilitates the leap from understanding the science to engineering its real-life application.”

Source

GeForce Now Ultimate first impressions: Streaming has come a really long way

Steve Jobs posing with the Lisa in 1983.

Ted Thai

 

Forty years ago today, a new type of personal computer was announced that would change the world forever. Two years later, it was almost completely forgotten.

 

The Apple Lisa started in 1978 as a new project for Steve Wozniak. The idea was to make an advanced computer using a bit-slice processor, an early attempt at scalable computing. Woz got distracted by other things, and the project didn’t begin in earnest until early 1979. That’s when Apple management brought in a project leader and started hiring people to work on it.

 

Lisa was named after Steve Jobs’ daughter, even though Jobs denied the connection and his parentage. But the more interesting thing about the Lisa computer was how it evolved into something unique: It was the first personal computer with a graphical user interface (GUI).

The vision takes shape

GUIs were invented at Xerox’s Palo Alto Research Center (PARC) in the early 1970s. The Alto workstation, which was never sold to the public, had a bitmapped screen that mimicked the size and orientation of a piece of paper. PARC researchers wrote software that displayed windows and icons, and they used a mouse to move a pointer on that screen.

 

Jef Raskin, an early Apple employee who wrote the manual for the Apple ][, had visited PARC in 1973. He believed that GUIs were the future. Raskin managed to persuade the Lisa project leader to change the computer into a GUI machine. However, he couldn’t convince Jobs, who thought Raskin and Xerox were incompetent.

 

Raskin altered his approach and got graphics programmer Bill Atkinson to propose an official tour of PARC in November 1979. Because Jobs thought Atkinson was great, he agreed to come along. Jobs’ visit to PARC became the stuff of legend, a tale of a brilliant visionary seeing the future of computing for the first time. But in reality, Atkinson was already working on LisaGraf—the low-level code that would power the Lisa’s GUI—months before Jobs saw the PARC demo.

 

The Lisa’s hardware changed as well. The team abandoned the bit-slice processor and adopted Motorola’s new 68000 CPU. The 68000 was a 16/32-bit chip and used a 24-bit address bus, giving it a maximum of 16 megabytes of memory. This was fine, as memory prices were still sky-high in 1980, and most computers of the day had a maximum of 64 kilobytes of RAM.

 

In January 1981, senior leadership at Apple got tired of Jobs’ constant interference and micromanagement of the Lisa project and officially removed him from the team. Jobs seethed, then took over a smaller skunkworks project being run by Raskin. This would become important later.

 

By early 1982, the Lisa hardware was mostly finalized. However, the software was still in flux. A team of designers—including Larry Tesler, who had left PARC to join Apple—had been busy doing tons of research, prototyping, and testing. The main question they had was: How should the Lisa’s GUI actually work?

 

• 

• 

• 

• 

• 

• 

 

In an article in Interactions magazine, designers Roderick Perkins, Dan Smith, and Frank Ludolph described how the Lisa’s interface changed from early prototypes to a familiar desktop with icons, then away from that model, then finally back to an icon-based, document-centric approach. The goal was to make the Lisa powerful and fun to use.

 

At long last, the Lisa was ready to be unveiled to the public. On January 19, 1983, Apple announced the computer, which it accurately described as “revolutionary.”

Revisiting Apple’s ill-fated Lisa computer, 40 years on

Steve Jobs posing with the Lisa in 1983.

Forty years ago today, a new type of personal computer was announced that would change the world forever. Two years later, it was almost completely forgotten.

 

The Apple Lisa started in 1978 as a new project for Steve Wozniak. The idea was to make an advanced computer using a bit-slice processor, an early attempt at scalable computing. Woz got distracted by other things, and the project didn’t begin in earnest until early 1979. That’s when Apple management brought in a project leader and started hiring people to work on it.

 

Lisa was named after Steve Jobs’ daughter, even though Jobs denied the connection and his parentage. But the more interesting thing about the Lisa computer was how it evolved into something unique: It was the first personal computer with a graphical user interface (GUI).

The vision takes shape

GUIs were invented at Xerox’s Palo Alto Research Center (PARC) in the early 1970s. The Alto workstation, which was never sold to the public, had a bitmapped screen that mimicked the size and orientation of a piece of paper. PARC researchers wrote software that displayed windows and icons, and they used a mouse to move a pointer on that screen.

 

Enlarge / A restored Xerox Alto, still running code in 2017.

Ken Sheriff

 

Jef Raskin, an early Apple employee who wrote the manual for the Apple ][, had visited PARC in 1973. He believed that GUIs were the future. Raskin managed to persuade the Lisa project leader to change the computer into a GUI machine. However, he couldn’t convince Jobs, who thought Raskin and Xerox were incompetent.

 

Raskin altered his approach and got graphics programmer Bill Atkinson to propose an official tour of PARC in November 1979. Because Jobs thought Atkinson was great, he agreed to come along. Jobs’ visit to PARC became the stuff of legend, a tale of a brilliant visionary seeing the future of computing for the first time. But in reality, Atkinson was already working on LisaGraf—the low-level code that would power the Lisa’s GUI—months before Jobs saw the PARC demo.

 

The Lisa’s hardware changed as well. The team abandoned the bit-slice processor and adopted Motorola’s new 68000 CPU. The 68000 was a 16/32-bit chip and used a 24-bit address bus, giving it a maximum of 16 megabytes of memory. This was fine, as memory prices were still sky-high in 1980, and most computers of the day had a maximum of 64 kilobytes of RAM.

 

In January 1981, senior leadership at Apple got tired of Jobs’ constant interference and micromanagement of the Lisa project and officially removed him from the team. Jobs seethed, then took over a smaller skunkworks project being run by Raskin.

This would become important later.

 

By early 1982, the Lisa hardware was mostly finalized. However, the software was still in flux. A team of designers—including Larry Tesler, who had left PARC to join Apple—had been busy doing tons of research, prototyping, and testing. The main question they had was: How should the Lisa’s GUI actually work?

 

June 1979: A mockup of an early Lisa interface, running on an Apple ][.

 Interactions magazine

 

August 1980: A mockup of menus and dialog boxes, running on a Lisa prototype.

 Interactions magazine

 

October 1980: A mockup of the new single menu bar. Note the “Note from Jef.”

 Interactions magazine

 

December 1980: An abandoned multi-column file browser. This design would return in NeXTstep and OS X.

 Interactions magazine

 

July 1981: The “Twenty Questions” file locator. It worked, but nobody found it “fun,” so the team returned to the icon-based approach.

 Interactions magazine

 

August 1982: The Lisa’s GUI is finally finalized.

 Interactions magazine

 

In an article in Interactions magazine, designers Roderick Perkins, Dan Smith, and Frank Ludolph described how the Lisa’s interface changed from early prototypes to a familiar desktop with icons, then away from that model, then finally back to an icon-based, document-centric approach. The goal was to make the Lisa powerful and fun to use.

 

At long last, the Lisa was ready to be unveiled to the public. On January 19, 1983, Apple announced the computer, which it accurately described as “revolutionary.”

Lisa meets the world

Enlarge / The original Lisa, as shipped with two floppies and a 5MB ProFile hard drive.

 

The Lisa was an all-in-one computer with a 12-inch monochrome screen at a resolution of 720×365 pixels. It used a Motorola 68000 CPU running at 5 MHz. It had 1MB of RAM. The computer came with two Apple-designed “Twiggy” 5.25-inch floppy drives, but it was designed to be used with Apple’s “ProFile” 5MB hard drive, which sat on top. It also had three expansion slots. The Lisa’s keyboard was attached by a coiled cord, and a mouse was included.

 

The decision to go with a one-button mouse was, according to Tesler, arrived at after extensive user testing. Because of this, the Lisa software designers had to invent a new form of interaction called “double-clicking.”

 

The Lisa came bundled with both an operating system and an “office suite” of seven applications: LisaWrite, a word processor; LisaCalc, a spreadsheet; LisaDraw, a vector drawing program; LisaList, a primitive database; LisaProject, a project management app; LisaTerminal, a terminal emulator that worked with a modem; and LisaGraph, a chart-maker program.

 

The operating system (Lisa OS) had preemptive multitasking and could run many programs at once. It also supported virtual memory and memory protection. To gain these features required a Memory Management Unit (MMU) chip, but Motorola hadn’t built one yet, so Apple had to design a solution on its own.

 

Enlarge / The internals of the Lisa. The Motorola 68000 CPU is in the top right. The two boards in front contain error-corrected RAM in 8KB chips, 512KB per board. Three expansion slots are on the bottom right.

8-Bit Show and Tell

 

All this revolutionary hardware and software was amazing, but it came at a steep cost: The Lisa’s price was $9,995, which would be nearly $30,000 today. This was too much for home users. Business owners—the target market for the Lisa—might pay this much, but they would expect a computer to support multiple users connected via cheap text-based terminals and to run boring accounting software, not esoteric design apps for budding graphic artists.

 

As a result, sales of the Lisa were slow. Only a few thousand computers were sold in the first year, and the numbers didn’t pick up much after that. They weren’t helped by Jobs running around telling everyone that the real revolutionary computer from Apple, the Macintosh, was just around the corner.

After being kicked off the Lisa team, Jobs strong-armed his way into Raskin’s Macintosh project. Raskin loved GUIs, but he had different ideas for his computer, which he wanted to be an inexpensive and easy-to-use appliance. After being forced out of the Mac team, Raskin resigned from Apple. Jobs liked the “appliance” metaphor, but he wanted the Macintosh to be a cheaper Lisa.

 

Jobs achieved this by taking the small Mac team and shuffling them into a separate building, putting a pirate flag on the roof, and then going on nerd raids to steal hardware, software, and engineers from the Lisa team.

 

Enlarge / Some of the original Mac team with their pirate flag.

Fortune

 

LisaGraf, the highly optimized set of graphics routines written by Atkinson, was renamed QuickDraw and lifted wholesale to the Mac. This was easy, since both machines used the 68000 processor. The single menu bar and the desktop and icon interface were also moved over, although they had to be redesigned a little. The main reason for this? Cost.

 

The $9,995 price of the Lisa seems outrageous compared to the $2,495 introductory price for the Mac. But think of what Jobs removed to hit that target. The Mac had only one floppy and no hard drive. The hard drive alone was a $3,499 add-on included in the Lisa’s price. The Mac had only 128KB of RAM, compared to 1MB in the Lisa, which was eight times as much and cost about $2,400 at the time. The Mac had a smaller 9-inch screen at a lower 512×342 pixel resolution. It had no bundled software other than MacWrite and MacPaint. This was because the software had to be rewritten in assembly language to hit the much smaller memory targets, and assembly language programming took much longer to write.

 

The Macintosh was also incompatible with software for the Lisa. New software had to be written for the Mac from scratch—although ironically, in the beginning, the only way to develop this software was on a Lisa.

 

Enlarge / The Lisa 2, later renamed the Macintosh XL.

Wikipedia

 

The Lisa 2 was released in 1984 at the same time as the Mac. It had a different case but was the same internally. It came with the now-standard (and more reliable) 3.5-inch floppy drive and had room inside for an MFM hard drive, which was becoming a popular choice in the PC world. It cost a more reasonable $3,495 to $5,495, and sales started to pick up.

But Jobs had deposed the executive who had originally thrown him off the Lisa team and now found himself in charge of both the Mac and the Lisa. He renamed the Lisa 2 the “Macintosh XL” and shipped it with software that emulated the Macintosh operating system instead of the Lisa software. In 1985, he discontinued the Lisa entirely. The remaining inventory was sold to a third party, and in 1989, Apple buried 2,700 Lisas in a landfill to get a tax break.

 

Enlarge / September, 1989. The end of the Lisa.

Herald Journal

What could have been

Hitting its cost target was a good thing for the Mac, which sold much better than the Lisa. But it turned the computer into a less functional machine. Multitasking was gone—the original Mac could only run one application at a time. Autosaving was also gone, as was virtual memory and memory protection and Lisa OS’s resilient file system. It took many years for these features to return, by which time the price of both memory and hard drives had plummeted. In an alternate universe, the Lisa’s price could have steadily decreased while its capabilities kept growing. For example, the Lisa team had plans to extend the single level of undo to an unlimited undo.

 

The main criticism of the Lisa, other than its price, was that the system was too slow. I did not find this to be the case—the overall speed was only slightly slower than the original Mac. Applications took longer to start up on the Lisa, but these applications also had more features. In any case, the speed differences would have gone away as CPUs became faster and compilers improved.

 

While some features of the Lisa’s interface, like clicking on a “paper” icon instead of an application to open a new document, were confusing, the computer set the standard for how GUIs would work for decades to come. The concept of menu bars and pull-down menus, double-clicking, the smart redrawing of overlapping windows, and even the idea of copy and paste was invented at Apple and first shipped in the Lisa.

 

The Lisa is a footnote in the history of computing. But today, 40 years after it was first announced, it reminds us that the best ideas don’t always win out, and that there are many lessons we can learn from the past.

 

Source

All good things must come to an end, and it’s best to plan beforehand. Revolutionary as the JWST has been, it has a much shorter life expectancy than the Hubble Space Telescope, let alone ground-based instruments. NASA has revealed plans for what could be the replacement to its replacement at the American Astronomical Society’s 241st meeting. 

Their proposal is for an instrument that could carry the space telescope torch after the Nancy Grace Roman Observatory (NGRO), which is scheduled to launch around 2027, about the time the JWST ceases operations.

The future telescope does not have its final name yet, but NASA’s Mark Clampin told the audience that the project is being planned as the Habitable Worlds Observatory (HWO). Neither details nor funding have been confirmed, but as its working name suggests the project will be much more focused on a single task than the JWST or Hubble.

The JWST is already examining the atmospheres of planets orbiting nearby stars. This, however, represents just a small part of its operations, with more time studying the most distant galaxies, star-forming regions in our own, and a host of other targets.

The HWO will be designed to look for “biosignatures”, gases in the atmospheres of rocky or watery planets within stars’ habitable zones.

The plans are designed to meet the call in astronomy’s decadal survey for an instrument capable of detecting signs of life on 25 nearby planets. Hopefully, it will find unambiguous evidence for a biologically created atmosphere before that – but 25 is considered sufficient to indicate life is very rare if none show positive signs.

Although it may have a clearer prime objective than its predecessors, the HWO will be capable of many other tasks. As probably the most powerful space telescope in operation for at least the initial years of its life, plenty of astronomers with other specialties will compete with exoplanet researchers for time directing it to their projects.

The HWO is expected to be a similar size to the JWST and likewise located at L2: the point 1.5 million kilometers (932,057 miles) from Earth where gravitational forces cancel out sufficiently for orbital stability.

The downside to this location is that repairs, upgrades, and refueling have been considered impractical for the JWST, which is why it's not expected to last more than ten years. Hubble, meanwhile, is in its third decade thanks to several maintenance missions to Low Earth Orbit. 

However, Clampin indicated that NASA thinks by the time the HWO launches, robot missions will be able to reach the site to service it. This will not only enable a much longer life than the JWST, but create the possibility of upgrades making it more powerful with time.

“Serviceability will be huge,” Aki Roberge of NASA’s Goddard Space Flight Center told ScienceNews. Although the mirrors would stay the same, the instruments used to process the light they collect could improve, creating what Roberge called a “mountaintop observatory at L2.”

The current HWO plans fall between two previous concepts NASA has floated in more detail, known as HabEx and LUVOIR.  It’s expected to build on the success of the JWST’s unfolding multi-component mirror, keeping to a similar scale, since we now know how to do this, and using features that will get a test run on the NGRO.

LUVOIR’s design is much bigger and more ambitious, but also likely to take longer and be more expensive, suggesting the HWO might be an intermediary. HabEx has been foreseen as having a single mirror, individually larger than any of the JWST’s and therefore capable of crisper images, but smaller than the combined power of the JWST’s 18.

Unsurprisingly, considering the early stage of the plans, there is no firm timeline for the HWO other than Clampin talking about it launching in the 2040s. Considering how many delays were required for the JWST, it may be just as well to provide a decade-long range, particularly with the limitations on this portion of NASA’s budget.

Many other important but smaller and more specialized space telescopes are, or recently have been, in operation, including Gaia,  Spitzer, Kepler and TESS.  However, when it comes to large instruments operating in the optical or near-optical part of the spectrum the HWO is intended to continue a dynasty that runs from Hubble to the JWST and then the NGRO.

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Scientists in the United States have developed a machine learning algorithm that can predict a surge of COVID-19 cases at county level across the US, in the vast majority of cases. Such a tool could have a powerful impact in protecting people, and let healthcare systems prepare up to six weeks before a major outbreak.

To reach this prediction level, the team fed the machine learning algorithm historical data on COVID-19 activity in 97 US counties, as well as search terms from Google, tweets, technical searches from medical professionals, and directions requested from Apple Maps. The algorithm was able to work out the relevant aspects that flagged the beginning of a surge in cases.

“We try to identify changes in human behavior that may be predictive of changes of transmission patterns in COVID-19,” corresponding author Professor Mauricio Santillana, from Harvard Medical School, told IFLScience. “When we are looking in real-time and we start seeing those patterns that historically have anticipated the emergence of an outbreak, then we can say, ‘okay, looks like an outbreak may happen in the coming one to six weeks’.”

The Centers for Disease Control and Prevention (CDC) already uses approaches to predict the evolution of COVID-19 infections and deaths over time. These are based on epidemiological indicators. The limitation of these models is that they often struggle to capture rapid changes, like during surges, because they do not receive fast, consistent, and reliable data.

“If we want to be responsive in real-time to outbreaks that are about to emerge, we want those models to be very accurate during those times when things change from being normal to a lot of cases,” Santillana explained to IFLScience.

This approach was able to identify 87 percent of the outbreaks in the data collected between January 2020 and January 2022. Compared to the standard CDC approach, the machine learning approach could predict outbreaks within a similar timeframe or up to six weeks in advance. The system has limitations when it comes to small and rural counties with low internet usage, but it is a step forward in predicting rapid changes in COVID-19 infections.

Santillana explained that such an approach could be used to help protect vulnerable people and to better allocate medical resources. In an ideal world, we might end up with a scenario similar to the Y2K scare, where the system's prediction is taken so seriously by everyone that the problem is solved before it materializes.

The work is published in the journal Science Advances.

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Logitech announces $70 webcams with USB-C, built-in shutters

Apple is reportedly working on an iPad-like smart display

Boston Dynamics’ bipedal robot Atlas is now tossing tool bags around a (fake) construction site

Last week, Twitter user Gael Breton noticed that CNET had been publishing articles written by artificial intelligence (AI). Writing in a thread, Breton noted that the publisher had started to test "automation technology" around 11 November 2022, with several articles on personal finance authored by algorithms.

Connie Guglielmo, editor-in-chief at CNET, defended the experiment in a post for CNET, writing that the idea was "to see if the tech can help our busy staff of reporters and editors with their job to cover topics from a 360-degree perspective".

"Will this AI engine efficiently assist them in using publicly available facts to create the most helpful content so our audience can make better decisions?" Guglielmo asked. "Will this enable them to create even more deeply researched stories, analyses, features, testing and advice work we're known for?"

Author “CNET Money” has written 78 articles so far, which CNET notes were all "reviewed, fact-checked and edited by our editorial staff". However, a few mistakes have slipped through. As spotted by Vice, the team at CNET have had to issue a correction to an explainer on compound interest, explaining several of the interest examples were incorrect.

"An earlier version of this article suggested a saver would earn $10,300 after a year by depositing $10,000 into a savings account that earns 3 percent interest compounding annually," the correction reads. "The article has been corrected to clarify that the saver would earn $300 on top of their $10,000 principal amount. A similar correction was made to the subsequent example."

Following a similar correction, the list continues.

"The earlier version also incorrectly stated that one-year CDs only compound annually. The earlier version also incorrectly stated how much a consumer would pay monthly on a car loan with an interest rate of 4 percent over five years. The earlier version also incorrectly stated that a savings account with a slightly lower APR, but compounds more frequently, may be a better choice than an account with a slightly higher APY that compounds less frequently. In that example, APY has been corrected to APR."

After being approached by Vice for comment, a press office contact replied that they were reviewing all their AI-assisted pieces to make sure no other mistakes were made in the "editing process, as humans make mistakes too".

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DeepL Write is available for free currently and labeled beta. The writing tool fixes grammar and punctuation mistakes, makes writing suggestions, and may rephrase entire sentences.

The interface resembles that of DeepL Translator. Users type or paste text into the text field on the right and DeepL Write its improved version on the right. The process is instantaneous and users may copy the result to the clipboard right away.

The option to display alternatives for  words is available. A click on a word or phrase displays alternatives, which the user may select with a click. The same option is available for sentences also. DeepL Write displays different versions of the sentence, which users may select to replace the selected one.

DeepL Write is available as a web tool only at this point. Language support is limited to English and German at the time. The writing companion supports British and American English, which users may select from a small menu at the top of the source text field.

The company's translation service started with limited language support as well. Additional languages and tools were added over the years, and the same is going to happen to DeepL Write. Expect languages like Spanish, French and Portuguese to be included in coming updates. Future updates may also include options to process entire documents and dedicated tools or add-ons to improve workflows and the usability.

DeepL has not revealed plans regarding a commercial version of the service. The company may target the freemium model again, which it uses for DeepL Translator already; this would keep a base version of DeepL Write free for anyone, but limited.

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However, whereas Microsoft looks set to pay its way to the leading positions in these emerging AI markets, Google has been continuing to develop its own AI products and the search giant looks set to launch, via its subsidiary DeepMind, a rival chatbot to ChatGPT in the near future. The new chatbot, which is called Sparrow, was introduced last year as a proof-of-concept in a research paper. DeepMind CEO Demis Hassabis said that Sparrow could be released for a "private beta" in 2023.

Hassabis also went on to mention why the project has been delayed compared to ChatGPT’s release saying that caution Is warranted when dealing with these types of products. This approach has certainly been borne out by the variety of risks associated with generative technologies like this, as has been the case since ChatGPT launched. As well as plaudits and high levels of what could legitimately be called hysteria, ChatGPT’s release has also caused alarm thanks to how easy it has been to get past the guardrails designed to prevent it from generating harmful and biased text outputs and even the fact that scammers have been using it to craft phishing emails and even malicious code.

Another key consideration that DeepMind is taking into account with Sparrow is factual accuracy. Essentially, when querying these types of language models, they output text that reads legitimately but that has only been created to look like what a real answer would be. The tools themselves don’t have any conception of whether what they have told you is true or not.

To help deal with this, Sparrow is expected to have important features such as the ability to cite specific sources of information. According to the research paper outlining how Sparrow works, it supported its plausible-sounding answers with evidence 78% of the time. DeepMind is also working on behavior-constraining rules and a willingness to defer to humans in certain contexts.

The above innovations sound like important steps for these types of tools. The addition of links to cited information definitely gives Sparrow a much more Google flavor than what you currently get from ChatGPT. As to how this will address some of the fears that have been raised relating to areas such as education remain to be seen, however. While citing sources that have been used to formulate answers could offer an excellent starting point for research, it could also come out like a ready-made essay for submission complete with relevant references.

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