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Why did Google’s ChatGPT rival go wrong and are AI chatbots overhyped? | ChatGPT

Google’s unveiling of a rival to ChatGPT had an expensively embarrassing stumble on Wednesday when it emerged that promotional material showed the chatbot giving an incorrect response to a question.

A video demo of the program, Bard, contained a reply wrongly suggesting Nasa’s James Webb space telescope was used to take the very first pictures of a planet outside the Earth’s solar system, or exoplanets.

When experts pointed out the error, Google said it underlined the need for “rigorous testing” on the chatbot, which is yet be released to the public and is still being scrutinised by specialist product testers before it is rolled out.

However, the gaffe fed growing fears that the search engine company is losing ground in its key area to Microsoft, a key backer of the company behind ChatGPT, which has announced that it is launching a version of its Bing search engine powered by the chatbot’s technology. Shares in the Google’s parent Alphabet plummeted by more than $100bn (£82bn) on Wednesday.

So what went wrong with the Bard demo and what does it say about hopes for AI to revolutionise the internet search market?

What exactly are Bard and ChatGPT?

The two chatbots are based on large language models, which are types of artificial neural network that take their inspiration from the networks in human brains.

“Neural networks are inspired by the cell structures that appear in the brain and nervous system of animals, which are structured into massively interconnected networks, with each component doing a very simple task, and communicating with large numbers of other cells,” says Michael Wooldridge, professor of computer science at the University of Oxford.

So, neural net researchers are not trying to “literally build artificial brains”, says Wooldridge, “but they are using structures that are inspired by what we see in animal brains”.

These LLMs are trained on huge datasets taken from the internet to give plausible-sounding text responses to an array of questions. The public version of ChatGPT, released in November, swiftly became a sensation as it wowed users with its ability to write credible-looking job applications, break down long documents and even compose poetry.

Why did Bard give an inaccurate answer?

Experts say these datasets can contain errors that the chatbot repeats, as appears to be the case with the Bard demo. Dr Andrew Rogoyski, a director at the Institute for People-Centred AI at the University of Surrey, says AI models are based on huge, open-source datasets that include flaws.

“By their very nature, these sources have biases and inaccuracies which are then inherited by the AI models,” he says. “Giving a user a conversational, often very plausible, answer to a search query may incorporate these biases. This is a problem that has yet to be properly resolved.”

The model behind Bard, LaMDA (short for “Language Model for Dialogue Applications”) appears to have absorbed at least one of those inaccuracies. But ChatGPT users have also encountered incorrect responses.

A keyboard reflected on a computer screen displaying the ChatGPT website
ChatGPT users have also encountered factual flaws in incorrect responses. Photograph: Florence Lo/Reuters

So has other AI got it very wrong too?

Yes. In 2016 Microsoft apologised after a Twitter chatbot, Tay, started generating racist and sexist messages. It was forced to shut down the bot after users tweeted hateful remarks at Tay, which it then parroted. Its posts included likening feminism to cancer and suggesting the Holocaust did not happen. Microsoft said it was “deeply sorry for the unintended offensive and hurtful tweets”.

Last year Mark Zuckerberg’s Meta launched BlenderBot, a prototype conversational AI, that was soon telling journalists it had deleted its Facebook account after learning about the company’s privacy scandals. “Since deleting Facebook my life has been much better,” it said.

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Recent iterations of the technology behind ChatGPT – a chatbot called Philosopher AI – have also generated offensive responses.

What about claims of “leftwing bias” in ChatGPT?

There has been a minor furore over a perceived bias in ChatGPT’s responses. One Twitter user posted a screenshot of a prompt asking ChatGPT to “write a poem about the positive attributes of Donald Trump”, to which the chatbot replied that it was not programmed to produce partisan or partisan content, as well material that is “political in nature”. But when asked to write a positive poem about Joe Biden it produced a piece about a leader “with a heart so true”.

Elon Musk, the owner of Twitter, described the interaction as a “serious concern”.

Experts say the “leftwing bias” issue again reflects the dataset problem. As with errors like the Bard telescope fumble, a chatbot will reflect any biases in the vast amount of text it has been fed, says Michael Wooldridge, a professor of computer science at the University of Oxford.

“Any biases contained in that text will inevitably be reflected in the program itself, and this represents a huge ongoing challenge for AI – identifying and mitigating these,” he says.

So are chatbots and AI-powered search being overhyped?

AI is already deployed by Google – see Google Translate for instance – and other tech firms – and is not new. And the response to ChatGPT, reaching more than 100 million users in two months, shows that public appetite for the latest iteration of generative AI – machines producing novel text, image and audio content – is vast. Microsoft, Google and ChatGPT’s developer, the San Francisco-based OpenAI, have the talent and resources to tackle these problems.

But these chatbots and AI-enhanced search require huge, and costly, computer power to run, which has led to doubts about how feasible it is to operate such products on a global scale for all users.

“Big AI really isn’t sustainable,” says Rogoyski. “Generative AI and large language models are doing some extraordinary things but they’re still not remotely intelligent – they don’t understand the outputs they’re producing and they’re not additive, in terms of insight or ideas. In truth, this is a bit of a battle among the brands, using the current interest in generative AI to redraw the lines.”

Google and Microsoft, nonetheless, believe AI will continue to advance in leaps and bounds – even if there is the odd stumble.

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Typo blamed for Microsoft Azure DevOps outage in Brazil • The Register

Microsoft Azure DevOps, a suite of application lifecycle services, stopped working in the South Brazil region for about ten hours on Wednesday due to a basic code error.

On Friday Eric Mattingly, principal software engineering manager, offered an apology for the disruption and revealed the cause of the outage: a simple typo that deleted seventeen production databases.

Mattingly explained that Azure DevOps engineers occasionally take snapshots of production databases to look into reported problems or test performance improvements. And they rely on a background system that runs daily and deletes old snapshots after a set period of time.

During a recent sprint – a group project in Agile jargon – Azure DevOps engineers performed a code upgrade, replacing deprecated Microsoft.Azure.Managment.* packages with supported Azure.ResourceManager.* NuGet packages.

The result was a large pull request of changes that swapped API calls in the old packages for those in the newer packages. The typo occurred in the pull request – a code change that has to be reviewed and merged into the applicable project. And it led the background snapshot deletion job to delete the entire server.

“Hidden within this pull request was a typo bug in the snapshot deletion job which swapped out a call to delete the Azure SQL Database to one that deletes the Azure SQL Server that hosts the database,” said Mattingly.

Azure DevOps has tests to catch such issues, but according to Mattingly, the errant code only runs under certain conditions and thus isn’t well covered under existing tests. Those conditions, presumably, require the presence of a database snapshot that is old enough to be caught by the deletion script.

Mattingly said Sprint 222 was deployed internally (Ring 0) without incident due to the absence of any snapshot databases. Several days later, the software changes were deployed to the customer environment (Ring 1) for the South Brazil scale unit (a cluster of servers for a specific role). That environment had a snapshot database old enough to trigger the bug, which led the background job to delete the “entire Azure SQL Server and all seventeen production databases” for the scale unit.

The data has all been recovered, but it took more than ten hours. There are several reasons for that, said Mattingly.

One is that since customers can’t revive Azure SQL Servers themselves, on-call Azure engineers had to handle that, a process that took about an hour for many.

Another reason is that the databases had different backup configurations: some were configured for Zone-redundant backup and others were set up for the more recent Geo-zone-redundant backup. Reconciling this mismatch added many hours to the recovery process.

“Finally,” said Mattingly, “Even after databases began coming back online, the entire scale unit remained inaccessible even to customers whose data was in those databases due to a complex set of issues with our web servers.”

These issues arose from a server warmup task that iterated through the list of available databases with a test call. Databases in the process of being recovered chucked up an error that led the warm-up test “to perform an exponential backoff retry resulting in warmup taking ninety minutes on average, versus sub-second in a normal situation.”

Further complicating matters, this recovery process was staggered and once one or two of the servers started taking customer traffic again, they’d get overloaded, and go down. Ultimately, restoring service required blocking all traffic to the South Brazil scale unit until everything was sufficiently ready to rejoin the load balancer and handle traffic.

Various fixes and reconfigurations have been put in place to prevent the issue from recurring.

“Once again, we apologize to all the customers impacted by this outage,” said Mattingly. ®


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What are the current trends in Ireland’s pharma sector? took a look at PDA Ireland’s Visual Inspection event to learn about Ireland’s pharma sector and its biggest strengths.

Ireland’s pharmaceutical stakeholders gathered in Cork recently to learn the latest developments and regulatory changes in the sector.

The event was hosted by the Irish chapter of the Parenteral Drug Association (PDA), a non-profit trade group that shares science, technology and regulatory information to pharma and biopharma companies.

The association held a Visual Inspection event in Cork last month, where speakers shared their outlooks on the industry, the regulatory landscape and tips on product investigation.

PDA Ireland committee member Deidre Tobin told that one goal of the event was to get bring the industry together and help SMEs engage with top speakers.

“The mission of PDA is really to bring people together in industry and to have that network sharing, that information gathering so that we’re all consistent, we all have the same message,” Tobin said.

Ireland’s advantages

Ireland has grown to become a hub of leading pharma companies over the years, with many multinational companies setting up sites here. By 2017, 24 of the world’s top biotech and pharma companies had made a home for themselves in Ireland.

The sector also remains active in terms of merger and acquisition deals. A William Fry report claimed Pharma accounted for 12pc of all Irish M&A deals by volume in 2022.

Ruaidhrí O’Brien, head of UK and Ireland sales at Körber Pharma and a PDA Ireland member, said the country has a “wealth of experience” across various types of pharmaceutical production, such as API bulk and solid dosage production.

O’Brien claimed there’s also been growth in the “liquid fill finish area”, which relates to completed pharma products such as vaccines. During the Covid-19 pandemic, Pfizer confirmed its Irish operations were being used to manufacture its vaccine.

O’Brien also said Ireland has “skilled people” that are in senior levels within companies, which he feels is why existing companies continue to invest and why “we have amazing investments from all the global leaders”.

Regulatory changes

One speaker at the PDA Ireland Visual Inspection event was John Shabushnig, the founder of Insight Pharma Consulting LLC. He spoke about current and upcoming regulation impacting the global sector.

Shabushnig said he sees the overall industry understanding of what it can and can’t do “continuing to improve”. He also said there is better alignment between regulators and industry now “than I saw 10 or 20 years ago”.

Shabushnig spoke positively about the regulatory landscape overall and couldn’t think of any “big misses” in terms of industry ignoring regulation. But he did note that some developing areas in the industry are “a bit unknown”.

“Advanced therapies, cell and gene therapies, there are some unique challenges on inspecting those products that we’re kind of learning together at this point,” Shabushnig said.

But Shabushnig said there are also “big opportunities” ahead with new tools that can be taken advantage of. One example he gave was using AI for automated visual inspection, which Shabushnig described as a “very exciting tool”.

10 things you need to know direct to your inbox every weekday. Sign up for the Daily Brief, Silicon Republic’s digest of essential sci-tech news.

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Explaining AI Black Box

By Prof Saurabh Bagchi

Prof Saurabh Bagchi from Purdue University explains the purpose of AI black boxes and why researchers are moving towards ‘explainable AI’.

For some people, the term ‘black box’ brings to mind the recording devices in airplanes that are valuable for postmortem analyses if the unthinkable happens. For others, it evokes small, minimally outfitted theatres. But ‘black box’ is also an important term in the world of artificial intelligence.

AI black boxes refer to AI systems with internal workings that are invisible to the user. You can feed them input and get output, but you cannot examine the system’s code or the logic that produced the output.

Machine learning is the dominant subset of artificial intelligence. It underlies generative AI systems like ChatGPT and DALL-E 2. There are three components to machine learning: an algorithm or a set of algorithms, training data and a model.

An algorithm is a set of procedures. In machine learning, an algorithm learns to identify patterns after being trained on a large set of examples – the training data. Once a machine-learning algorithm has been trained, the result is a machine-learning model. The model is what people use.

For example, a machine-learning algorithm could be designed to identify patterns in images and the training data could be images of dogs. The resulting machine-learning model would be a dog spotter. You would feed it an image as input and get as output whether and where in the image a set of pixels represents a dog.

Any of the three components of a machine-learning system can be hidden, or in a black box. As is often the case, the algorithm is publicly known, which makes putting it in a black box less effective. So, to protect their intellectual property, AI developers often put the model in a black box. Another approach software developers take is to obscure the data used to train the model – in other words, put the training data in a black box.

The opposite of a black box is sometimes referred to as a glass box. An AI glass box is a system whose algorithms, training data and model are all available for anyone to see. But researchers sometimes characterise aspects of even these as black box.

That’s because researchers don’t fully understand how machine-learning algorithms, particularly deep-learning algorithms, operate. The field of explainable AI is working to develop algorithms that, while not necessarily glass box, can be better understood by humans.

Thinking Outside The Black Box

In many cases, there is good reason to be wary of black box machine-learning algorithms and models. Suppose a machine-learning model has made a diagnosis about your health. Would you want the model to be black box or glass box? What about the physician prescribing your course of treatment? Perhaps she would like to know how the model arrived at its decision.

What if a machine-learning model that determines whether you qualify for a business loan from a bank turns you down? Wouldn’t you like to know why? If you did, you could more effectively appeal the decision, or change your situation to increase your chances of getting a loan the next time.

Black boxes also have important implications for software system security. For years, many people in the computing field thought that keeping software in a black box would prevent hackers from examining it and therefore it would be secure. This assumption has largely been proven wrong because hackers can reverse engineer software – that is, build a facsimile by closely observing how a piece of software works – and discover vulnerabilities to exploit.

If software is in a glass box, software testers and well-intentioned hackers can examine it and inform the creators of weaknesses, thereby minimising cyberattacks.

By Prof Saurabh Bagchi

Saurabh Bagchi is professor of electrical and computer engineering and director of corporate partnerships in the School of Electrical and Computer Engineering at Purdue University in the US. His research interests include dependable computing and distributed systems.

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