Profits Over Promises: IBM Sells Facial Recognition Tech to British Government
September 18, 2023
Just three years after it swore off any involvement in facial recognition software, IBM has made an about-face. The Verge reports, “IBM Promised to Back Off Facial Recognition—Then it Signed a $69.8 Million Contract to Provide It.” Amid the momentous Black Lives Matter protests of 2020, IBM’s Arvind Krishna wrote a letter to Congress vowing to no longer supply “general purpose” facial recognition tech. However, it appears that is exactly what the company includes within the biometrics platform it just sold to the British government. Reporter Mark Wilding writes:
“The platform will allow photos of individuals to be matched against images stored on a database — what is sometimes known as a ‘one-to-many’ matching system. In September 2020, IBM described such ‘one-to-many’ matching systems as ‘the type of facial recognition technology most likely to be used for mass surveillance, racial profiling, or other violations of human rights.'”
In the face of this lucrative contract IBM has changed its tune. It now insists one-to-many matching tech does not count as “general purpose” since the intention here is to use it within a narrow scope. But scopes have a nasty habit of widening to fit the available tech. The write-up continues:
“Matt Mahmoudi, PhD, tech researcher at Amnesty International, said: ‘The research across the globe is clear; there is no application of one-to-many facial recognition that is compatible with human rights law, and companies — including IBM — must therefore cease its sale, and honor their earlier statements to sunset these tools, even and especially in the context of law and immigration enforcement where the rights implications are compounding.’ Police use of facial recognition has been linked to wrongful arrests in the US and has been challenged in the UK courts. In 2019, an independent report on the London Metropolitan Police Service’s use of live facial recognition found there was no ‘explicit legal basis’ for the force’s use of the technology and raised concerns that it may have breached human rights law. In August of the following year, the UK’s Court of Appeal ruled that South Wales Police’s use of facial recognition technology breached privacy rights and broke equality laws.”
Wilding notes other companies similarly promised to renounce facial recognition technology in 2020, including Amazon and Microsoft. Will governments also be able to entice them into breaking their vows with tantalizing offers?
Cynthia Murrell, September 18, 2023
A Perfect Plan: Mainframes Will Live Forever
September 7, 2023
Note: This essay is the work of a real and still-alive dinobaby. No smart software involved, just a dumb humanoid.
Experienced COBOL programmers are in high demand and short supply, but IBM is about to release an AI tool that might render that lucrative position obsolete. The Register reports: “IBM Says GenAI Can Convert that Old COBOL Code to Java for You.” Dubbed the watsonx Code Assistant for Z, the tool should be available near the end of this year. Reporter Dan Robinson gives us a little background:
“COBOL supports many vital processes within organizations globally – some that would surprise newbie devs. The language was designed specifically to be portable and easier for coding business applications. The good news is that it works. The bad news is it’s been working for a little long. COBOL has been around for over 60 years, and many of the developers who wrote those applications have since retired or are no longer with us. ‘If you can find a COBOL programmer, they are expensive. I have seen figures showing they can command some of the highest salaries because so many mission critical apps are written in COBOL and they need maintenance,’ Omdia Chief Analyst Roy Illsley told us.
Migrating the code to Java means there are many more programmers around, he added, and if the apps run on Linux on Z then they can potentially be moved off the mainframe more easily in future.”
Perhaps. There are an estimated 775 to 850 billion lines of COBOL code at work in the business world, and IBM is positioning Code Assistant to help prioritize, refactor, and convert them all into Java. There is just one pesky problem:
“IBM is not the only IT outfit turning to AI tools to help developers code or maintain applications, however, the quality of AI-assisted output has been questioned. A Stanford University study found that programmers who accepted help from AI tools like Github Copilot produce less secure code than those who did not.”
So maybe firms should hold on to those COBOL programmers’ contact info, just in case.
Cynthia Murrell, September 7, 2023
IBM and Smart Software: Try and Try Again, Dear Watson with an X
August 7, 2023
Note: This essay is the work of a real and still-alive dinobaby. No smart software involved, just a dumb humanoid.
With high hopes, IBM is acquiring FinOps firm Apptio for $4.6 billion. As Horses for Sources puts it, “IBM’s Acquisition of Apptio Can Shine if IBM Software and IBM Consulting Work Together to Deliver Cost-Managed Innovation at Speed.” But that is a big “if”. The odds seem long from the standpoint of RedHat users unimpressed with both IBM’s approach and internal cooperation at the company.
The young, sincere child presages her future in a giant technology company, “Yes, I will try to stack blocks to make the big building you told me to create with the blocks I got from my friend, Apt Ti Oh.” MidJourney, you did let me down with your previous “frustrated kid” images. Sultry teens were not what I was after.
IBM intends to mix Apptio with several other acquisitions that have gone into the new Watsonx platform, like Turbonomic, Instana, and MyInvenio, to create a cohesive IT-management platform. Linking spending data with operational data should boost efficiency, save money, and facilitate effective planning. This vision, however, is met with some skepticism. Writers Tom Reuner and Phil Fersht tell us:
“Apptio never progressed beyond providing insights, while IBM needs to demonstrate the proof points for integrating its disparate capabilities as well as progress from insight to action and, ultimately, automation. IBM Software must work with IBM Consulting transformation more effectively. … In essence, if successful, the ability to act on – and ultimately automate – all those insights is pretty much the operational Holy Grail. Just for transparency, getting expansive spend management and FinOps capabilities in itself will be a solid asset for IBM. However, any new and bolder proposition aiming at the bigger transformation price must move beyond technology and include stakeholders and change management. The ambition could be a broader business assurance where spend data, operational insights, and governance get tied to business objectives. In our view, this provides a significant alignment opportunity with IBM Consulting as it seeks to differentiate itself from the likes of Accenture Operations and Genpact. Having a deep services alignment with Watsonx and Apptio will bridge together the ability to manage the cost and value of both cloud transformation and AI investments – provided it gets it right with its global talent base of technical and process domain specialists.”
So the objective is a platform that brings companies’ disparate parts together into a cohesive and efficient whole. But this process must involve humans as well as data. If IBM can figure out how to do so within its own company, perhaps it stands a chance of reaching the goal.
Cynthia Murrell, August 6, 2023
Quantum Seeks Succor Amidst the AI Tsunami
July 5, 2023
![Vea4_thumb_thumb_thumb_thumb_thumb_t[1]](http://arnoldit.com/wordpress/wp-content/uploads/2023/07/Vea4_thumb_thumb_thumb_thumb_thumb_t1-5.gif "Vea4_thumb_thumb_thumb_thumb_thumb_t[1]")
Note: This essay is the work of a real and still-alive dinobaby. No smart software involved, just a dumb humanoid.
Imagine the heartbreak of a quantum wizard in the midst of the artificial intelligence tsunami. What can a “just around the corner” technology do to avoid being washed down the drain? The answer is public relations, media coverage, fascinating announcements. And what companies are practicing this dark art of outputting words instead of fully functional, ready-to-use solutions?
Give up?
I suggest that Google and IBM are the dominant players. Imagine an online ad outfit and a consulting firm with mainframes working overtime to make quantum computing exciting again. Frankly I am surprised that Intel has not climbed on its technology stallion and ridden Horse Ridge or Horse whatever into PR Land. But, hey, one has to take what one’s newsfeed delivers. The first 48 hours of July 2023 produced two interesting items.
The first is “Supercomputer Makes Calculations in Blink of an Eye That Take Rivals 47 Years.” The write up is about the Alphabet Google YouTube construct and asserts:
While the 2019 machine had 53 qubits, the building blocks of quantum computers, the next generation device has 70. Adding more qubits improves a quantum computer’s power exponentially, meaning the new machine is 241 million times more powerful than the 2019 machine. The researchers said it would take Frontier, the world’s leading supercomputer, 6.18 seconds to match a calculation from Google’s 53-qubit computer from 2019. In comparison, it would take 47.2 years to match its latest one. The researchers also claim that their latest quantum computer is more powerful than demonstrations from a Chinese lab which is seen as a leader in the field.
Can one see this fantastic machine which is 241 million times more powerful than the 2019 machine? Well, one can see a paper which talks about the machine. That is good enough for the Yahoo real news report. What do the Chinese, who have been kicked to the side of the Information Superhighway, say? Are you joking? That would be work. Writing about a Google paper and calling around is sufficient.
If you want to explore the source of this revelation, navigate to “Phase Transition in Random Circuit Sampling.” Note that the author has more than 175 authors is available from ArXiv.org at https://arxiv.org/abs/2304.11119. The list of authors does not appear in the PDF until page 37 (see below) and only about 80 appear on the abstract page on the ArXiv splash page. I scanned the list of authors and I did not see Jeff Dean’s name. Dr. Dean is/was a Big Dog at the Google but …
Just to make darned sure that Google’s Quantum Supremacy is recognized, the organizations paddling the AGY marketing stream include NASA, NIST, Harvard, and more than a dozen computing Merlins. So there! (Does AGY have an inferiority complex?)
The second quantum goody is the write up “IBM Unlocks Quantum Utility With its 127-Qubit “Eagle” Quantum Processing Unit.” The write up reports as actual factual IBM’s superior leap frogging quantum innovation; to wit, coping with noise and knowing if the results are accurate. The article says via a quote from an expert:
The crux of the work is that we can now use all 127 of Eagle’s qubits to run a pretty sizable and deep circuit — and the numbers come out correct
The write up explains:
The work done by IBM here has already had impact on the company’s [IBM’s] roadmap – ZNE has that appealing quality of making better qubits out of those we already can control within a Quantum Processing Unit (QPU). It’s almost as if we had a megahertz increase – more performance (less noise) without any additional logic. We can be sure these lessons are being considered and implemented wherever possible on the road to a “million + qubits”.
Can one access this new IBM approach? Well, there is this article and a chart.
Which quantum innovation is the more significant? In terms of putting the technology in one laptop, not much. Perhaps one can use the system via the cloud? Some may be able to get outputs… with permission of course.
But which is the PR winner? In my opinion, the Google wins because it presents a description of a concept with more authors. IBM, get your marketing in gear. By the way, what’s going on with the RedHat dust up? Quantum news releases won’t make that open source hassle go away. And, Google, the quantum stuff and the legion of authors is unlikely to impress European regulators.
And why make quantum noises before a US national holiday? My hunch is that quantum is perfect holiday fodder. My question, “When will the burgers be done?”
Stephen E Arnold, July 5, 2023
The Return: IBM Watsonx!
May 26, 2023
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Note: This essay is the work of a real and still-alive dinobaby. No smart software involved, just a dumb humanoid.
It is no surprise IBM’s entry into the recent generative AI hubbub is a version of Watson, the company’s longtime algorithmic representative. Techspot reports, “IBM Unleashes New AI Strategy with ‘watsonx’.” The new suite of tools was announced at the company’s recent Think conference. Note “watsonx” is not interchangeable with “Watson.” The older name with the capital letter and no trendy “x” is to be used for tools individuals rather than company-wide software. That won’t be confusing at all. Writer Bob O’Donnell describes the three components of watsonx:
“Watsonx.ai is the core AI toolset through which companies can build, train, validate and deploy foundation models. Notably, companies can use it to create original models or customize existing foundation models. Watsonx.data, is a datastore optimized for AI workloads that’s used to gather, organize, clean and feed data sources that go into those models. Finally, watsonx.governance is a tool for tracking the process of the model’s creation, providing an auditable record of all the data going into the model, how it’s created and more.Another part of IBM’s announcement was the debut of several of its own foundation models that can be used with the watsonx toolset or on their own. Not unlike others, IBM is initially unveiling a LLM-based offering for text-based applications, as well as a code generating and reviewing tool. In addition, the company previewed that it intends to create some additional industry and application-specific models, including ones for geospatial, chemistry, and IT operations applications among others. Critically, IBM said that companies can run these models in the cloud as a service, in a customer’s own data center, or in a hybrid model that leverages both. This is an interesting differentiation because, at the moment, most model providers are not yet letting organizations run their models on premises.”
Just to make things confusing, er, offer more options, each of these three applications will have three different model architectures. On top of that, each of these models will be available with varying numbers of parameters. The idea is not, as it might seem, to give companies decision paralysis but to provide flexibility in cost-performance tradeoffs and computing requirements. O’Donnell notes watsonx can also be used with open-source models, which is helpful since many organizations currently lack staff able build their own models.
The article notes that, despite the announcement’s strategic timing, it is clear watsonx marks a change in IBM’s approach to software that has been in the works for years: generative AI will be front and center for the foreseeable future. Kinda like society as a whole, apparently.
Cynthia Murrell, May 26, 2023
IBM Embraces a Younger Hot Number. Tough Luck, Watson, You Old Dog, You
May 12, 2023
Note: This essay is the work of a real and still-alive dinobaby. No smart software involved, just a dumb humanoid.
That outstanding newspaper, The New York Post, published “IBM Pauses Hiring for 7,800 Jobs Because They Could Be Performed by AI.” The story picks up where the dinobaby tale ends. As you may recall, IBM decided that old timers could train contractors and then head to the old age home. The evictees were dubbed “dinobabies.” As a former supplier to IBM, I eagerly adopted the moniker and use an anigif to illustrate how spritely a dinobaby can be.
The new approach to work at IBM, according to the estimable newspaper, is smart software, not smart software elder uncle. The article states:
Krishna said that the company will either slow down or altogether suspend hiring for so-called “back office” functions such as human resources.
Back office functions is not defined. Perhaps it will include [a] junior and mid level programmers, [b] customer facing engineers who do Zoom type calls demonstrating sympathy and technical skills in looking up information in Big Blue’s proprietary technical databases, [c] some annoying MBAs who churn out slide decks and viewpoints about how to make IBM young again, and [d] non essential personnel like expensive old lawyers, assorted strategic planners working on the old money machines like the mainframes, and annoying design professionals who want to add L.E.D.s to IBM’s once speed champion super computers.
But whose AI will Big Blue embrace? My hunch is that it will be a combination of the forward forward technology employed by a few renegade researchers who embraced Google methods and open source software which could be dressed up with a RedHat business model. You may have a different idea. I am sticking with mine, thank you, until IBM reveals its new, rejuvenated self after a weekend in the Bahamas with its new bestie or is it best-ai?
Who says you can teach an old dog how to do an old trick with a new bone? Not me. And Watson? Who?
Stephen E Arnold, May 12, 2013
New Hardware for Smart Software from IBM
November 2, 2022
IBM is getting into the AI hardware acceleration game with its new Artificial Intelligence Unit (AIU), we learn from VentureBeat‘s piece, “IBM Announces System-On-Chip AI Hardware.” Each AIU holds 32 cores similar to the Telum chip’s AI core. Rather than a CPU or GPU, the new component is an application-specific integrated circuit (ASIC) designed with AI in mind. This allows it to perform tasks not part of many AI accelerators, we’re told, like the ability to virtualize AI acceleration services. We are assured it is compatible with “the vast majority” of software commonly used by data scientists.
So far so good, but we noticed something in a passage tucked at the end of the write-up—it almost seems results are merely close enough for horseshoes and hand grenades. In order to work faster, the AIU practices “approximate computing.” Kerner tells us:
“Approximate computing is really the recognition that AI is not 100% correct,’ Leland Chang, principal research staff member and senior manager, AI hardware, at IBM Research, told VentureBeat. Chang explained that AI often works by recognizing a pattern and could well be just 99% accurate, meaning that 1% of results are incorrect. The concept of approximate computing is the recognition that within the AI algorithm it is possible to cut some corners. While Chang admitted that this can reduce precision, he explained that if information is lost in the right places, it doesn’t affect the result — which, more often than not, will still be 99% correct. ‘Approximate computing … is simply recognizing that it doesn’t have to be 100% exact,’ Chang said. ‘You’re losing some information, but you’re losing in places where it doesn’t matter.'”
You don’t say. Can we get a guarantee of that? Who makes the electronic components? Oh, right. Bad question.
Cynthia Murrell, November 2, 2022
IBM Data Governance Tools
October 21, 2022
Confused about data governance? Just rely on IBM. That is our takeaway from a write-up at TechRepublic, “An Overview of IBM Data Governance Solutions.” Author Aminu Abdullahi begins by describing IBM’s top data tools, though whether “top” here means most popular or most heavily promoted is unknown. First up is Cloud Pak, a cloud-based AI platform made to gather and analyze data from multiple sources. OpenPages both guides users in protecting sensitive data and manages compliance issues. To wrest BI insights from data, users can turn to InfoSphere Optim. Then, of course, there is everything Watson. The post explains the framework that pulls it all together:
“As an organization grows, it’s important to have a plan to protect and manage data. The IBM data governance framework is a set of best practices that helps businesses create an overarching strategy for managing the life cycle of their data. IBM’s data governance practice framework includes four types of control:
- Ensure: Controls for guiding work.
- Assure: Controls for doing work.
- Insure: Controls for operating.
- Reassure: Controls for continuity.
These controls allow companies to identify, protect, manage, monitor and report on their data. They do this by working with their business leaders, functional heads and IT teams across the organization to create unified standards for how companies should use information from creation through disposal. For example, the Identify phase will help establish roles and responsibilities for stakeholders within the organization; Protect will provide guidelines for how to store all types of data securely; Manage can help ensure high-quality information; Monitor can give insight into what’s happening with information assets. Finally, Report covers tools that generate comprehensive reports on all aspects of data management. The framework helps build an environment where accountability and responsibility are clear across the enterprise.”
So IBM is a one-stop shop for responsible and profitable data management, if you will. The post concludes by noting these tools have received rave reviews from current users. We wonder, though, how many of those users have any basis for comparison. We ask, “Can’t Watson do this?”
Cynthia Murrell, October 21, 2022
The Push for Synthetic Data: What about Poisoning and Bias? Not to Worry
October 6, 2022
Do you worry about data poisoning, use of crafted data strings to cause numerical recipes to output craziness, and weaponized information shaped by a disaffected MBA big data developer sloshing with DynaPep?
No. Good. Enjoy the outputs.
Yes. Too bad. You lose.
For a rah rah, it’s sunny in Slough look at synthetic data, read “Synthetic Data Is the Safe, Low-Cost Alternative to Real Data That We Need.”
The sub title is:
A new solution for data hungry AIs
And the sub sub title is:
Content provided by IBM and TNW.
Let’s check out what this IBM content marketing write up says:
One example is Task2Sim, an AI model built by the MIT-IBM Watson AI Lab that creates synthetic data for training classifiers. Rather than teaching the classifier to recognize one object at a time, the model creates images that can be used to teach multiple tasks. The scalability of this type of model makes collecting data less time consuming and less expensive for data hungry businesses.
What are the downsides of synthetic data? Downsides? Don’t be silly:
Synthetic data, however it is produced, offers a number of very concrete advantages over using real world data. First of all, it’s easier to collect way more of it, because you don’t have to rely on humans creating it. Second, the synthetic data comes perfectly labeled, so there’s no need to rely on labor intensive data centers to (sometimes incorrectly) label data. Third, it can protect privacy and copyright, as the data is, well, synthetic. And finally, and perhaps most importantly, it can reduce biased outcomes.
There is one, very small, almost miniscule issue stated in the write up; to wit:
As you might suspect, the big question regarding synthetic data is around the so-called fidelity — or how closely it matches real-world data. The jury is still out on this, but research seems to show that combining synthetic data with real data gives statistically sound results. This year, researchers from MIT and the MIT-IBM AI Watson Lab showed that an image classifier that was pretrained on synthetic data in combination with real data, performed as well as an image classifier trained exclusively on real data.
I loved the “seems to show” phrase I put in bold face. Seems is such a great verb. It “seems” almost accurate.
But what about that disaffected MBA developer fiddling with thresholds?
I know the answer to this question, “That will never happen.”
Okay, I am convinced. You know the “we need” thing.
Stephen E Arnold, October 6, 2022
IBM Power10 Rah Rah: One Concerning Statement
September 12, 2022
IBM is back in the marketing game. Everyone wants a Power10 computer in a mobile phone or a MacBook Air form factor. Am I right! Yes.
The article “IBM Power10 Shreds Ice Lake Xeons for Transaction Processing.” This is a big iron made less big. The article points out use cases for those AIX users. Plus there are references to notable big iron outfits like Oakridge and Lawrence Livermore Labs, both really common computing environments like those in the local Coca-Cola distributor’s office or the regional garbage outfit’s offices in three cities.
The charts are phenomenal. Here’s an example. Look at how the blue bar is lower than the gray bar. And the power savings and the thermal data? You know what air conditioners are for as well as those nifty Caterpillar generators in the parking lot are for, don’t you?
Very encouraging.
But…
I noticed one sentence which gave me pause; to wit:
IBM will, of course, make some competitive wins, mostly in emerging markets (and in some cases as Inspur sells iron in China), and it will also win some deals for new kinds of workloads like MongoDB, EnterpriseDB, or Redis.
With the export restrictions imposed by the US on China, will the Power10 find its way to the Middle Kingdom? The use cases for Power10 at US national laboratories may exist in a country wrestling with some real estate issues. Can the Power10 help with the land and construction challenges? What about Chinese academics-only, please research outfits?
In the midst of a PR type content marketing article, I found the reference to China interesting. Will anyone else?
Stephen E Arnold, September 12, 2022
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