Tag Archives: Machine learning

February 11, 2025 · 22:46

Just Asking

Today I started to ask questions within me. I have been an outspoken critic on the fact of AI and knowing it doesn’t exist questions came to mind. Question that, as I see it the BBC isn’t asking either. So lets get to this game and let you work out what is real.

Phase One
In phase one we look at AI and the data, you see any deeper machine learning solution (whether you call it AI or not) will depend on data. Now we get that no matter what you call this solution it will require data. Now that Deeper Machine Learning and LLM solutions require data (as well as the fact that the BBC is throwing article after article at us) who verifies the data?

Consider that these solutions have access to all that data, how can any solution (AI or not) distinguish the relevant data? We get the BBC in January give us this quote “That includes both smaller, specialist AI-driven biotech companies, which have sprung up over the past decade, and larger pharmaceutical firms who are either doing the research themselves, or in partnership with smaller firms.” My personal issue is that they all want to taste from the AI pie and there are many big and small companies vying for the same slice. So who verifies the data collected? If any entry in that data sphere requires verification, what stops errors from seeping through? This could be completely unintentional, but it will happen. And any Deeper Machine Learning system cannot inspect itself. It remains a human process. We will be given a whole range of euphemistic settings to dance around that subject, but in short. When that question is asked, the medical presenter is unlikely to have the answer and the IT person might dance around the subject. Only once did I get a clear answer from a Chinese data expert “We made an assumption on the premise of the base line according to the numbers we have had in the past”, which was a decent answer and I didn’t expect that answer making it twice as valuable. There is the trend that people will not know the setting and in the now there is as I see it, a lack of verification. 

Phase Two
Data Entry is a second setting. As the first is the verification of data that is handled, the second question is how was this data entered? It is that setting and not the other way round. You must have verifiable data to get to the data entry part. If you select a million parameters, how can you tell if a parameter is where it needs to be? And then there is a difference between intrinsic and extrinsic data. What is observed and what is measured. Then we get to the stage that (as the most simple setting) that are the Celsius and Fahrenheit numbers correct (is there a C when if should be an F) you might think that it is obvious, but there are settings when that is a definite question mark. Again, nothing intentional, but the question remains. So when we consider that and Deeper Machine Learning comes with a guidance and all this comes from human interactions. There will be questions and weirdly enough I have never seen them or seen anyone ask this (looking way beyond the BBC scope).

Phase Three
This is a highly speculative part. You see environment comes into play here and you might have seen it on a vacation. Whilst the locals enjoy market food, you get a case of the runs. This is due to all kinds of reasons. Some are about water and some about spices. As such the locals are used to the water and spices but you cannot handle either. This is an environmental setting. As such the data needs to be seen with personal medical records and that is a part we often do not see (which makes sense), but in that setting how can any solution make a ‘predicted’ setting when part of that data is missing?

So, merely looking at these three settings. I have questions and before you think I am anti-AI. I am not, it merely doesn’t exist yet and whilst the new Bazooka Joe’s are hiding behind the cloak of AI, consider that all this require human intervention. From Data Entry, to verification and the stage of environmental factors. So do you really think that an Indian system will have the same data triggers as a Swedish one? And consider that I am merely asking questions, questions the BBC and many others aren’t seemingly asking.

So take a moment to let that shift in and consider how many years we are away from verified data and now consider all the claims you see in the news. And this is only the medical field. What other fields have optionally debatable data issues?

Have a great day and when Mr. Robot say all is well, make sure you get a second opinion from a living GP. 

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December 6, 2024 · 12:09

Early Christmas for Guerrilla Software

Yes, that happens. The moment that we hand someone an early Christmas. The fact that Guerrilla software is not Microsoft related and the fact that they inspired this idea made me want to give me the idea to them. In this it all started on November 9th when I saw something that woke up a spark of innovation. It got me to write ‘The Easy Lesson’ (at https://lawlordtobe.com/2024/11/09/the-easy-lesson/) and when I read the statement “Reports suggest that development on Vision Pro began in late 2015, and from that time until WWDC, Apple filed for over twenty thousand worldwide patents and spent about $130 billion on R&D.” I tasted a massive hint of negativity there. I forgot who wrote it, but the idea that innovation was slapped down because it costed a little (130 time a little) threw me off. I thought, what can I do to make it a stronger success. I get it that reporter was all about being cozy with the place where ‘free’ money is (aka Facebook) and I decided to counter that and here is the result, all freely available for Guerrilla software as well as Apple who could use a rather large win at this time. So here it is and have at it.

The idea
The idea is not a game but a visual exploration based on the game. You see, no matter how excellent the game is (and it is really good) true emersion is seen when you are in the middle of it all and as such Apple Vision Pro makes it’s introduction into the world. The idea is to use the setting of the game to show the vision holder on how immersive the Apple Vision Pro is. In this narration you are a traveller from somewhere else. You start in Mother’s Heart. It gives you a lesson in how the narrative works. You are an ‘inventor’ of the camera and as such you can set the stage. You can walk freely in Mother’s Heart see the people and interact with them. The game gives you tasks and that gets you credit coins. If you complete all tasks you get a red marker from an elder. The red marker lets you travel to another location. It also gives you a shelter. You get it randomly, but the shelter is in your name. If you do not have a shelter in that place you get a bunk in an inn or place (depends on where you are). So lets have a look at the locations.

  • Mother’s Heart
  • Freeheap
  • Sunstone rock
  • Meridian
  • Sunfall
  • Mainspring (option)
  • Ban-Ur (option)
  • Song’s edge
  • Longnotch
  • GreyCatch

Mothers Heart has one new location (still random), all others have 2-3 locations

When you are in your location you get tasks (of a sort). You are given a ride (mostly striders in first part) and the narration is set to your proving your camera. You are given an escort a son or daughter of Aloy. AshTone (Daughter) or BeeSneeze (Son). They will escort you so that the ride will ‘stay’ in the right place. Each location has rides out of town to locations where the machines are. They will have an old location to visit, machines to see and more of that. The important part is that this is not a game. You see the machines, but they are all docile. You will be able to photograph yourself with the machine in the background and you haven’t seen anything until you see yourself with a Thunderjaw or a Storm-bird in the background. It will be to get the good shots with machines or distinctive locations in the background. In this we could also enable to locations with a holograph in view and the views they had in the game. There will be the need to add a few hundred tasks in the game so that any location will have dozens of tasks but per ‘play day’ you only get 10. When 10 are completed, you get a red marker and in the first location (Mother’s heart) you get an assigned location, via a raffle bag, which will have stones. Each stoner is engraved and  signifies a location. At that point you will be able to travel to another location and start anew.

Each location will have a specific task, like only Mothers heart will have the option to see Devil’s Thirst. And each location should have a tall neck assignment. The idea is that the Tall neck and other large machines will show you these large machines through the Vision Pro making them seem a lot more impressive than on the PlayStation. All machines are docile and will not harm or attack you. There is however a setting with corrupted machines making the machines attack them on sight, the chance of that is a mere 1%, making it a rare setting. All these options make for playability and a long term entertainment setting. I wonder how long it will take for the Game map to be transferred to Vision Pro. And at this point I have enough setting to get Horizon Zero Dawn transferred including Frozen wastes. And in this the Forbidden West as well. I reckon that if this could be completed there would still be time until the third game is released. 

The towns should be near exact (wherever possible). Several ruins and old cities and each locations will have Chargers, Striders and Broadheads that can be ridden. As I see it, from the Mothers Heart (location one) Striders are used. From location 2 onwards Striders take you back and Chargers and Broadheads take you forward to another location. And after location 2, you can see the glyph on the machine to see where you will go. The locations you have already seen will be readable, the scribbled glyphs are indication that it is a new location and your focus hasn’t learned it yet. After the second location you will have 3-5 rides to chose from. And every 20 tasks after the first 10 give you an additional place to live and show off your created artwork. I have more on this, but that is for the eyes of Guerrilla only.

What I tried to envision is an original narrative with the locations all Horizon players loved and now a lot more ‘realistically’ seen through the Apple Vision Pro. As for the ‘creator’ aka ‘het Grobbekuiken’ Mathijs de Jonge. Hier is het idee, als je denkt dat het wat is, zie het als een ‘early Christmas present’, Veel plezier en een prettig uiteinde. 

Have a great day, it’s Friyay!

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September 20, 2024 · 20:58

The step in the open

We all have this, we see another option. Or an option that is there when two players unite their resources. So here we get player two who enters the field. The player has an app named Talkie. It is seemingly some hardwired to a sexy girl that entices you to interact. She dances and possibly does more, but it is all virtual. The app gives us (as one of the options) “Enjoy endless conversations with Talkie AI’s expansive gallery”, well that seems to be the core of it.

Yet, already there was a player one in the air. It is FunEasyLearn. They offer a interesting way to learn languages. They appeared in the first covid phase, or at least that was when I became aware of them. They have a multitude of languages. Arabic, Chinese, Dutch, Finnish, French (my reason for getting the app), Greek, Hebrew, Italian, Japanese and several more. I got the plus package which was at the time $100 and it gave me instant access to every language they had. It was a good deal. 

Now combine those two (and add one language) and you get a new setting. 

In my case I would get an option to practice Latin (or ancient Greek). We could see this as an option for people to train language skills to men and women. Even until recently (2000 years ago) you would address a person (male or female) different depending on what their status was and believe me, the idea of addressing a roman centurion with the words ‘bonum mane carus’ as a simple slave, a senator or a striking woman will have very different responses (me laughing out loud). But with talkie in the field the options to learn linguistic skills gets a whole new range of options. Then there is the business need to learn Arabic and that is one business meeting you want to go well. The same can be said for every language. And here we have a new setting. Will FunEasyLearn offer it as a new stage, or will talkie add to its library of options? Consider the option to practice the classical languages (Greek and Latin). With that in option there is a lack of educational options and at present we need to hand more as a language tools, especially languages. Oh, and if you want to address Lord Hades with ‘εσύ εκεί, πού να είμαι’, let me know because that is an interaction I want to see with my own eyes. Should be fun for all seeing that interaction. 

All fun aside, there is more and more lacking in the proper interactions we all have (I am equally guilty in that) and now I see two apps that could score a lot higher by uniting skills. And the question is, was this option missed entirely, or is it still coming? 

Your guess is as good as mine, but the advertisement show us that enticing hormonal drifts (read: sex sells) might seem more prevalent, but even in France there is a case to address an person properly (I once hear a person use ca va, in stead of comment vas-tu), that one mistake ended the business introduction. The visiting man had taken offence. I do not know French as such I was unaware of what I had just missed. Wouldn’t it have been a great idea if these apps were in existence then? 

Just a simple snack for thought. But some apps are growing a lot beyond the simple needs we have and as such we should hand the applications a larger width to offer the users a lot more.

Well, Saturday is still three hours away, as such I am going to kill a lot of Uruks (shadow of Mordor). We all need a hobby to keep us entertained. Have a great day.

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September 8, 2024 · 01:09

Poised to deliver critique

That is my stance at present. It might be a wrong position to have, but it comes from a setting of several events that come together at this focal point. We all have it, we are all destined to a stage of negativity thought speculation or presumption. It is within all of us and my article 20 hours ago on Microsoft woke something up within me. So I will take you on a slightly bumpy ride.

The first step is seen through the BBC (at https://www.bbc.com/worklife/article/20240905-microsoft-ai-interview-bbc-executive-lounge) where we get ‘Microsoft is turning to AI to make its workplace more inclusive’ and we are given “It added an AI powered chatbot into its Bing search engine, which placed it among the first legacy tech companies to fold AI into its flagship products, but almost as soon as people started using it, things went sideways.” With the added “Soon, users began sharing screenshots that appeared to show the tool using racial slurs and announcing plans for world domination. Microsoft quickly announced a fix, limiting the AI’s responses and capabilities.” Here we see the collective thoughts an presumptions I had all along. AI does not (yet) exist. How do you live with “Microsoft quickly announced a fix”? We can speculate whether the data was warped, it was not defined correctly. Or it is a more simple setting of programmer error. And when an AI is that incorrect does it have any reliability? Consider the old data view we had in the early 90’s “Garbage In, Garbage Out”. Then. We are offered “Microsoft says AI can be a tool to promote equity and representation – with the right safeguards. One solution it’s putting forward to help address the issue of bias in AI is increasing diversity and inclusion of the teams building the technology itself”, as such consider this “promote equity and representation – with the right safeguards” Is that the use of AI? Or is it the option of deeper machine learning using an LLM model? An AI with safeguards? Promote equity and representation? If the data is there, it might find reliable triggers if it knows where or what to look for. But the model needs to be taught and that is where data verification comes in, verified data leads to a validated model. As such to promote equity and presentation the dat needs to understand the two settings. Now we get the harder part “The term “equity” refers to fairness and justice and is distinguished from equality: Whereas equality means providing the same to all, equity means recognising that we do not all start from the same place and must acknowledge and make adjustments to imbalances.” Now see the term equity being used in all kinds of places and in real estate it means something different. Now what are the chances people mix these two up? How can you validate data when the verification is bungled? It is the simple singular vision that Microsoft people seem to forget. It is mostly about the deadline and that is where verification stuffs up. 

Satya Nadella is about technology that understands us and here we get the first problem. When we consider that “specifically large-language models such as ChatGPT – to be empathic, relevant and accurate, McIntyre says, they needs to be trained by a more diverse group of developers, engineers and researchers.” As I see it, without verification you have no validation and you merely get a bucket of data where everything is collected and whatever the result of it becomes an automated mess, hence my objection to it. So as we are given “Microsoft believes that AI can support diversity and inclusion (D&I) if these ideals are built into AI models in the first place”, we need to understand that the data doesn’t support it yet and to do this all data needs to be recollected and properly verified before we can even consider validating it. 

Then we get article 2 which I talked about a month ago the Wired article (at https://www.wired.com/story/microsoft-copilot-phishing-data-extraction/) we see the use of deeper machine learning where we are given ‘Microsoft’s AI Can Be Turned Into an Automated Phishing Machine’, yes a real brain bungle. Microsoft has a tool and criminals use it to get through cloud accounts. How is that helping anyone? The fact that Microsoft did not see this kink in their trains of thought and we are given “Michael Bargury is demonstrating five proof-of-concept ways that Copilot, which runs on its Microsoft 365 apps, such as Word, can be manipulated by malicious attackers” a simple approach of stopping the system from collecting and adhering to criminal minds. Whilst Windows Central gives us ‘A former security architect demonstrates 15 different ways to break Copilot: “Microsoft is trying, but if we are honest here, we don’t know how to build secure AI applications”’ beside the horror statement “Microsoft is trying” we get the rather annoying setting of “we don’t know how to build secure AI applications”. And this isn’t some student. Michael Bargury is an industry expert in cybersecurity seems to be focused on cloud security. So what ‘expertise’ does Microsoft have to offer? People who were there 3 weeks ago were shown 15 ways to break copilot and it is all over their 365 applications. At this stage Microsoft wants to push out broken if not an unstable environment where your data resides. Is there a larger need to immediately switch to AWS? 

Then we get a two parter. In the first part we see (at https://www.crn.com.au/news/salesforces-benioff-says-microsoft-ai-has-disappointed-so-many-customers-611296) CRN giving us the view of Marc Benioff from Salesforce giving us ‘Microsoft AI ‘has disappointed so many customers’’ and that is not all. We are given ““Last quarter alone, we saw a customer increase of over 60 per cent, and daily users have more than doubled – a clear indicator of Copilot’s value in the market,” Spataro said.” Words from Jared Spataro, Microsoft’s corporate vice president. All about sales and revenue. So where is the security at? Where are the fixes at? So we are then given ““When I talk to chief information officers directly and if you look at recent third-party data, organisations are betting on Microsoft for their AI transformation.” Microsoft has more than 400,000 partners worldwide, according to the vendor.” And here we have a new part. When you need to appease 400,000 partners things go wrong, they always do. How is anyones guess but whilst Microsoft is all focussed on the letter of the law and their revenue it is my speculated view that corners are cut on verification and validation (a little less on the second factor). And the second part in this comes from CX Today (at https://www.cxtoday.com/speech-analytics/microsoft-fires-back-rubbishes-benioffs-copilot-criticism/) where we are given ‘Microsoft Fires Back, Rubbishes Benioff’s Copilot Criticism’ with the text “Jared Spataro, Microsoft’s Corporate Vice President for AI at Work, rebutted the Salesforce CEO’s comments, claiming that the company had been receiving favourable feedback from its Copilot customers.” At this point I want to add the thought “How was that data filtered?” You see the article also gives us “While Benioff can hardly be viewed as an objective voice, Inc. Magazine recently gave the solution a D – rating, claiming that it is “not generating significant revenue” for its customers – suggesting that the CEO may have a point” as well as “despite Microsoft’s protestations, there have been rumblings of dissatisfaction from Copilot users” when the dust settles, I wonder how Microsoft will fare. You see I state that AI does not (yet) exist. The truth is that generative AI can have a place. And when AI is here, when it is actually here not many can use it. The hardware is too expensive and the systems will need close to months of testing. These new systems that is a lot, it would take years for simple binary systems to catch up. As such these LLM deeper machine learning systems will have a place, but I have seen tech companies fire up sales people and get the cream of it, but the customers will need a new set of spectacles to see the real deal. The premise that I see is that these people merely look at the groups they want, but it tends to be not so filtered and as such garbage comes into these systems. And that is where we end up with unverified and unvalidated data points. And to give you an artistic view consider the following when we use a one point perspective that is set to “a drawing method that shows how things appear to get smaller as they get further away, converging towards a single “vanishing point” on the horizon line” So that drawing might have 250,000 points. Now consider that data is unvalidated. That system now gets 5,000 extra floating points. What happens when these points invade the model? What is left of your art work? Now consider that data sets like this have 15,000,000 data points and every data point has 1,000,000 parameters. See the mess you end up with? Now go look into any system and see how Microsoft verifies their data. I could not find any white papers on this. A simple customer care point of view, I have had that for decades and Jared Spataro as I see it seemingly does not have that. He did not grace his speech with the essential need of data verification before validation. That is a simple point of view and it is my view that Microsoft will come up short again and again. So as I (simplistically) see it. Is by any chance, Jared Spataro anything more than a user missing Microsoft value at present?

Have a great day.

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January 20, 2024 · 15:53

The tables are starting to turn

This is a setting I always saw coming.It wasn’t magic or predestination, it was simple presumption. Presumption is speculation based on evidence, on facts. The BBC puts out a near perfect article (at https://www.bbc.co.uk/news/technology-67986611) where we see ‘What happens when you think AI is lying about you?’ There are several brilliant sides to it, as such it is best to read it for yourself. But I will use a few parts of it because there is a larger playing field in consideration. The first to realise is that AI does not exist, not yet. 

As such when we see ““Illegal content… means that the content must amount to a criminal offence, so it doesn’t cover civil wrongs like defamation. A person would have to follow civil procedures to take action,” it said. Essentially, I would need a lawyer. There are a handful of ongoing legal cases round the world, but no precedent as yet.”

This is actually a much larger setting then people realise. You see “AI algorithms are only as objective as the data they are trained on, and if that data is biased or incomplete, the algorithm will reflect those biases” Yet the larger truth is that AI does not exist, it is Machine Learning or better, as such it took a programmer, a programmer implies corporate liability. That is what corporations fear, that is why everything is as muddled as possible. I reckon that Google, Microsoft and all others making AI claims are fearing. You see when you consider “The second told me I was in “unchartered territory” in England and Wales. She confirmed that what had happened to me could be considered defamation, because I was identifiable and the list had been published. But she also said the onus would be on me to prove the content was harmful. I’d have to demonstrate that being a journalist accused of spreading misinformation was bad news for me.” I believe it is a little less simple than that. You see algorithm implies programming, as such the victim has a right to demand the algorithm be put out in court for scrutiny. The lines that resulted in defamation should be open to scrutiny and that is what big-tech fears at present, because AI does not exist. It is all based on collected data and that data should be verified by the legal team of the victim and that stops everything for the revenue hungry corporations. 

In addition I would like to add an article, also by the BBC (at https://www.bbc.co.uk/news/technology-68025677) called ‘DPD error caused chatbot to swear at customer’. It clearly implies that a programmer was involved. If language skills involve swearing, who put the swear words there? When did your youngest one start to swear? They all do at some point. So what triggered this? Now consider that machine learning requires data, so where is that swear data coming from? Who inclined or instituted that to be used? So when you see ““An error occurred after a system update yesterday. The AI element was immediately disabled and is currently being updated.” Before the change could be made, however, word of the mix-up spread across social media after being spotted by a customer. One particular post was viewed 800,000 times in 24 hours, as people gleefully shared the latest botched attempt by a company to incorporate AI into its business.” Consider that AI does not exist, consider that swear words are somehow part of that library, then consider that a programmer made a booboo (this is always allowed to happen) and they are ‘updating’ this. A system is being updated to use a word library. Now consider the two separate events as one and see how much danger the revenue hungry corporations have placed themselves in. When you go by ‘Trust but verify’ we can make all kinds of assumptions, but data is the centre of that core with two circles forming a Venn diagram. One circle is data, the other is programming. Now watch how big-tech is worried, because when this goes wrong, it goes wrong in a big way and they would be accountable for billions in pay outs. It will not be a small amount and it will be almost everywhere. The one case of a defamed journalist is one and in this day and age not the smallest setting. The second is that these systems will address customers. Some will take offence and some will take these companies to court. So how much funds did they think that they could safe with these systems? All to save on a dozen employees? A setting that will decide the fate of a lot of companies and that is what some fear. Until the media and several other dodo’s start realising that AI doesn’t yet exist. At that point the court cases will explode. It will be about a firm, their programmer and the wrong implementation of data. I reckon that within 2-3 years there will be an explosion of defamation cases all over the world. The places relying on Common Law will probably be getting more and sooner than Civil Law nations, but they will both face a harsh reality. It is all gravy whilst the revenue hungry sales people are involved. When the court cases come shining through those firms will have to face harsh internal actions. That is speculation on my side, but based on the data I see at present it seems like a clear case of  precise presumption which is what the BBC in part is showing us, no matter how courts aren’t ready. In torts there are cases and this is a setting staged on programmers and data, no mystery there and that could cost those hiding behind AI are facing. It is merely my point of view, but I feel that I am closer to the truth than many others evangelising whatever they call AI.

Enjoy the weekend.

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January 19, 2024 · 20:19

The other colour

That is what is ended up being. It started with the thoughts of ‘Pink is the colour of ignorance’, a story that might still make it, but I want to add more evidence. The Guardian had a good start, but it is more than that and I need to tag it. The other colour is green, the colour of dollars. Reuters give us some parts of it, but my mind is asking questions. Questions aren’t voiced by the media at present. As such we start with Reuters (at https://www.reuters.com/technology/google-invest-1-billion-uk-data-centre-2024-01-18/) where we are given ‘Google to invest $1 billion in UK data centre’ and this comes with the added text “It also comes weeks after Microsoft unveiled plans to pump 2.5 billion pounds ($3.2 billion) into Britain over three years, including in growing its data centre capacity, to underpin future AI services.” The math doesn’t work, especially now. You see UK pushed away from the EU and all this sets a weird station. I know that any data centre costs money and I have no idea how much. One argument is that a data center of the size that Facebook or Google might use would cost from $250 million to $500 million, so why is Google spending twice that and why is Microsoft spending 250% more than that? Now the twice I could get. Operational cost, rising energy costs and when you add that up you might get to 750 million and that is only 250 million away from the leap that Google is stating. 

Sp when you look at that setting we see two bulls fighting for the same population (Google and Microstupid) but the larger question becomes is why? Why spend that much to cater to 68 million people in the United Kingdom. It is not just services, it is data and data collection. To what degree is anyones guess, but wonder why Microsoft would spend $2,500,000,000 to service 68 million people. I am wondering who is buttering the sandwich of whom. I tend to distrust Microsoft, there have been too many issues and they have lost too many battles. Is this desperation? 

The open field
The questions in the open field is not the UK, you see if these two are there they are already growing in the Middle East or they are about to. You see, these investments make sense in the UAE with 9.5 million and Saudi Arabia with 36 million. Apart from their populations both these players will have exploding infrastructure needs (The Kingdom of Saudi Arabia more than the UAE), but the UAE is on a steep incline of services and services needs and I showed that in a few articles last week. The UK has none of that at present. More importantly the EU has also needs but not to these degrees and the UK facilities will have projected limitations as one might guess. So what gives? As for Future AI services. AI does not yet exist and the Machine Learning solutions are all massively dependent on data, something Microsoft is still short of. As I ponder more sides to this, I see more issues and also Huawei now has a data center in Abu Dhabi, giving them a much larger advantage in a place where cash is still king, or better stated cash has a more robust voice, more than the UK can muster at any given time before January 2026. 

As such there are issues and even as none of this is on Reuters (important to know), the setting is that the lack of visibility in several directions make me wonder where these two are going. No matter how good we think of Google (I still do) they both need data, Google to remain top dog and Microsoft to not be as irrelevant as they made themselves to be. 

Sides no one is looking at and I merely wonder why. Are they in a flim flam spin by Microsoft marketing? Do too many believe the shallowness of Microsoft presentations? Your guess is as good as mine, but when you start digging into actual sources that remain true non-biassed the math does not add up. At least for me it does not and I am not economist or econometrical engineer. Data is its own currency, the problem is that when it is the only currency remaining those who have it get access to everything, the rest do not.

Just a thought, enjoy your Friday.

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December 25, 2023 · 16:01

How stupid could stupid become?

Yup that was the question and it all started with an article by the CBC. I had to read it twice because I could not believe my eyes. But yes, I did not read it wrong and that is where the howling began. Lets start at the beginning. It all started with ‘Want a job? You’ll have to convince our AI bot first’, the story (at https://www.cbc.ca/news/business/recruitment-ai-tools-risk-bias-hidden-workers-keywords-1.6718151) gives us “Ever carefully crafted a job application for a role you’re certain that you’re perfect for, only to never hear back? There’s a good chance no one ever saw your application — even if you took the internet’s advice to copy-paste all of the skills from the job description” this gives us a problem on several factors, but the two I am focussing on is IT and recruiters. IT is the first. AI does not exist, not yet at least. What you see are all kinds of data driven tools, primarily set to Machine Learning and Deeper Machine Learning. First off, these tools are awesome. In their proper setting they can reduce workloads and automate CERTAIN processes.

But these machines cannot build, they cannot construct and they cannot deconstruct. To see whether a resume and a position match together you need the second tier, the recruiter (or your own HR department). There are skills involved and at times this skill is more of an art. Seeing how much alike a person is to the position is an art. You can test via a resume of minimum skills are available. Yes, at times it take a certain amount of Excel levels, it might take SQL skill levels or perhaps a good telephone voice. A good HR person (or recruiter) can see this. Machine Learning will not ever get it right. It might get close. 

So whilst we laugh at these experts, the story is less nice, the dangers are decently severe. You see, this is some side of cost reduction, all whilst too many recruiters have no clue what they are doing, I have met a boatload of them. They will brush it off with “This is what the client wants” but it is already too late, they were clueless from the start and it is getting worse. The article also gives  us a nice handle “They found more than 90 per cent of companies were using tools like ATS to initially filter and rank candidates. But they often weren’t using it well. Sometimes, candidates were scored against bloated job descriptions filled with unnecessary and inflexible criteria, which left some qualified candidates “hidden” below others the software deemed a more perfect fit.” It is the “they often weren’t using it well”, you see any machine learning is based on a precise setting, if the setting does not fit, the presented solution is close to useless. And it goes from bad to worse. You see it is seen with “even when the AI claims to be “bias-free.”” You see EVERY Machine learning solution is biased. Bias through data conversion (the programmer), bias through miscommunication (HR, executive and programmer misalignment) and that list goes on. If the data is not presented correctly, it goes wrong and there is no turning back. As such we could speculate that well over 50% of firms using ATS are not getting the best applicant, they are optionally leaving them to real recruiters, and as such handing to their competitors. Wouldn’t that be fun? 

So when we get to “So for now, it’s up to employers and their hiring teams to understand how their AI software works — and any potential downsides” which is a certain way to piss your pants laughing. It is a more personal view, but hiring teams tend to be decently clueless on Machine Learning (what they call AI). That is not their fault. They were never trained for this, yet consider what they are losing out of? Consider a person who never had military training, you now push them in a war stage with a rifle. So how long will this person be alive? And when this person was a scribe, how will he wield his weapon? Consider the man was a trompetist and the fun starts. 

The data mismatches and keeps this person alive by stating he is not a good soldier, lucky bastard. 

The foundation is data and filling jobs is the need of an HR department. Yes, machine learning could optionally reduce the time going through the resume’s. Yet bias sets in at age, ageism is real in Australia and they cannot find people? How quaint, especially in an aging population. Now consider what an executive knows about a job (mostly any job) and what HR knows and consider how most jobs are lost to translation in any machine learning environment. 

Oh, and I haven’t even considered some of these ‘tests’ that recruiters have. Utterly hilarious and we are given that this is up to what they call AI? Oh, the tears are rolling down my cheeks, what fun today is, Christmas day no less. I haven’t had this much fun since my fathers funeral.

So if you wonder how stupid can get, see how recruiters are destroying a market all by themselves. They had to change gears and approach at least 3 years ago. The only thing I see are more and more clueless recruiters and they are ALL trying to fill the same position. And the CBC and their article also gives us this gem “it’s also important to question who built the AI and whose data it was trained on, pointing to the example of Amazon, which in 2018 scrapped its internal recruiting AI tool after discovering it was biased against female job applicants.” So this is a flaw of the lowest level, merely gender. Now consider that recruiters are telling people to copy LinkedIn texts for their resume. How much more bias and wrong filters will pop up? Because that is the result of a recruiter too, they want their bonus and will get it anyway they can. So how many wrong hires have firms made in the last year alone? Amazon might be the visible one, but that list is a lot larger than you think and it goes to the global corporate top. 

So consider what you are facing, consider what these people face and laugh, its Christmas.

Enjoy today.

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October 21, 2023 · 20:21

Setting a Sunny Saturday

I was there, there was a yellow disc in the sky (aka the sun), I was sitting and merely contemplating stuff when I got hit with a video. 

It was 60 minute with something on underwater smuggling and how people were unprepared. It took me 15 seconds to set that premise to solved. OK, Google or Amazon need to get involved. It is not ‘that’ easy, but that is what Deeper Machine Learning is for. Funny enough, my ships engineering skills (outdated since 1981) got into field and my thought patterns resembled one I had in UTS when I came up for a system to weed out false positives in bomb detection. Whilst everyone was focussing on where the bomb was, I decided to look at a way to remove false positives which took mere seconds and when you have 4 million passengers a year, having certain points where you can scan a passenger in less than 5 seconds matters. The fact that you weed out 80% of the false positives also matters as it suddenly leaves you with a manageable number of people and with Deeper Machine Learning that system merely gets to be more accurate, as such within months that number would have increased to 90%+ which makes is an possibility. It was merely a concept but I was happy (as was my professor). Now we get back to the story. You see, it took seconds to find this puppy.

Here we have a commercial Japanese solution of a underwater drone. It is not enough, because we have to tinker with it and to make a drone an autonomous underwater vehicles (AUVs) takes work and the battery would require an update, the function and the added hardware will be murder on the regularly installed battery. The nice part is that these puppies do not need sleep and they could scan the hull of any vessel in minutes. Two might get it done in a minute and now we get the setting, a set of two one to scan and one to validate the scanning by weeding out the false negatives. Hulls are simple, they are one setting, they are smooth and waterproofed. The idea that a hull is tampered with is not laughable, but it tends to be slightly ridiculous, as such an ‘adjusted’ hull is noticed by any AUV and teaching it a few additional things is not hard, not for the right Deeper Machine Learning expert. As such we need to consider like an autonomous underwater vehicles (AUVs). You see a place like New York might have millions of containers a year, but it does remain a relatively small about of vessels, as such a dozen drones would be able to scan all the vessels BEFORE they dock and that is the busiest port in the world. The drones could also be scanning for other things, like divers going on a tourist tour past any vessel which would be a big no-no. 

These settings alone were solved (by little old me) in less than a minute, so why were these methods not considered? Perhaps they were and they found a snag I never considered. I am not prefect, but I try to see the solution in a challenge, not the hiccup.

Still the exercise was fun for the minute I had it, it gave me something else to consider for a moment. And when you think on how I got there, wonder what else I can come up with tomorrow, but that is a setting I will consider in 18 hours. The drone will need adjustments too, scanners on the top (two sets) facing 30 degrees up and 90 degrees up, it also needs to be altered into an autonomous underwater vehicle (AUV), which will a little work. So when we added the initial and verification scan, we get a vessel with the ability to do it at the same time and it is done in seconds per 10 metres. The learning curve needs to be adjusted and it can be set against type of vessel. You see a coaster, a tanker and a cargo ship have slightly difference hulls, but the same principle applies, waterproof or sink. It is really that simple at times. The smugglers ‘adding’ a box at the hull will fall through the hoops in the initial minute and as such the boys in blue (with flippers) can capture the haul. The ones who were clever and added a ‘valve’ to allow the merchandise to sit between the outer and inner hull is a little harder, but when Machine Learning considers that these valves should not be there, the pattern adjusts  as well. This will create some initial false positives, but there is also the gain that we eliminated 90% of all vessels making this a relatively easy exercise.

Wow, 3 minutes of my brain productively used. I am getting good at my old age. So consider this a concept, consider this a joke, it is all up to you and the boys in blue.  I did my bit on Saturday and I am not going to get paid for it, so use it as you see fit.

I am now 230 minutes from Sunday, have fun and enjoy the sunshine (if there is any).

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September 11, 2023 · 19:48

Folly and opportunity

Yup, a setting that has both. You see yesterday I offered the quote “I made mention of Deeper Machine Learning. This is awesome, it is not AI (AI does not yet exist) but it got me thinking. You see, we now see mention of AI in construction. This is about to go bad, really bad and Trusting these buildings will become folly soon enough. I will try to explain that soon enough” and that soon is now. To see this we need to make a few sidesteps, but it will be clear soon enough. For this I selected ‘Building a smarter future: The impact of big data and AI in construction’ (at https://www.pbctoday.co.uk/news/digital-construction-news/big-data-and-ai-in-construction-trimble/132005/) there are several sources, but this one got a few things really right and that matters to me. They give you “Because computers can be programmed to analyse questions and situations using thousands of parameters in the time it takes most of us to type them in, they’re an incredible tool that we can use to do complex calculations in a fraction of the time it takes any human, and because they approach every situation with logic, they can make the most rational decisions even when we can’t. Artificial intelligence in construction simply takes that to the next level, applying machine learning, which allows those same computers to learn from situations they’ve encountered before and to adjust their results accordingly.” I do not fully agree, but they give a better explanation then most others and they made the big good one by giving us ‘applying machine learning’ this is correct. 

Why is this what?
That is the setting, you see to see this I will need to take you on a little time travel. That is after you realise that machine learning depends on data, loads of it. But in all this the right category is also important. We are about to overlap best practice and best results onto the cheaper way, the cutting corners way. We might rely on movies like the towering inferno (1974) where the movie based on two books namely the Glass inferno and the tower. In the movie we see the bastardly electrical engineer who cut corners (played by Richard Chamberlain) and the architect played by Paul Newman. There we see the little conversation that the electrical engineer Roger Simmons kept to building codes and that the demands by the architect Doug Roberts were outlandish and to cost driving and fair enough, the building burns down on opening night.

Children of Mediocrates
The previous one was a story, fiction. But reality is not. In the 90’s captains of industry shook hands with politicians and a lacking drive was introduced. Almost like the philosopher Mediocrates who introduced a new life lesson ‘Meh, good enough’. I was actually in some of those meetings where we were told. “What if the strive of excellence is not 100%, but 80%. What had is it to be still really good. How much easier is it to build your bonus when we expect a 80% line?” I was there, I heard it all and I was told to adhere to it all. And yes the bonus for me was easier and I was merely in customer service, but it felt wrong. 

Nowadays
So back to today when we look at the application of what some call AI (a wrong term). The data it relies on cannot tell the difference because best practice and cutting corners are all the same thing and it will set a flawed recommendation and the larger folly is that the people in control of that data will not distinguish between the two fronts either. They are to young to tell, or they cannot tell the difference, because those filling their pockets are no longer around. It is a recipe for disaster and when was the last time when construction disasters went without casualties? 

This is the setting I see coming and there is also an opportunity. You see, those cutting corners did not protect the original path. As such these patents and IP points are now open and unprotected. As such these options are there for the clever people to create new innovation patents based on the open original patents, the ones the cutting corners people let be and there should be a fair amount of them all over the field. This is merely because best practice was too expensive for them and now those options are open. An example here might be the Reinforced autoclaved aerated concrete (RAAC). We are now seeing all the issues and the hundreds of buildings that have them. It was an invention in the 1990’s, making the timeline fit. And now we see “Concerns were amplified in 2023 following reports of an earlier roofing collapse at a British primary school, which fell without warning in 2018” Now, one does not mean the other, but there is a premise that fits and as such we see the larger danger. Consider that this all gained popularity in the 50’s. So how many new patents were created based on this idea, and what was left behind and unprotected? I will let you do the math, but whomever has those innovation patents will have the option to fill there pockets with the best practice approach whilst too many are merely in it to make a buck. As such the folly of hiding behind AI is about to hit a lot of people squarely in the face, all whilst the clever people will be able to turn a coin as they have the patents and they will be the only player to be considered soon enough.

Hiding behind hyper words suddenly gives others a chance to become serious players where the big boys never wanted them. How is that for poetic justice?

Enjoy the day, most of the week is still in front of you.

 

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September 10, 2023 · 21:05

As ideas evolve

This is a story with a few sides. The most prominent side is based on the continuation of Ludum Scriptor, which I wrote 2 days ago (at https://lawlordtobe.com/2023/09/08/ludum-scriptor/) there is set out a new premise, one that could have larger benefits. You see, as I was evolving certain ideas. One of them was to give football and fantasy football a new tool to provide their thoughts for progressing their game.

An old game for football addicts was Subutteo. We forgot about the old ideas, but they were good ideas. Now consider that with Deeper Machine Learning we an create any football game and as they are virtual and not based on plastic, they will look a lot more like the players. Any team in the world. Football, NFL, NBA, NHL and that list goes on. People can write and blog about their teams, they can write it in any way they want and that was when the wheels went in overdrive. You see, player cards and all kinds of other means could be made available for bloggers all over the world. And that list does not stop, not for some time. You see Deeper Machine learning as a tool for something like I wrote can do more and YOUR imagination can only drive it further.

Why Microsoft will fail
That was my premise and I kept on referring to a chihuahua stating ‘Try Azure, Azure smells nice’ was only to some degree a joke. But someone on LinkedIn gave me an idea.

You see being on par for a year gets you 1 (or 1365), but the smallest increase gets you to 37.7, 37 times the one you were one year later. And then there is the decrease. Even when you consider 0.99365. You end up with a mere 0.03, that is the difference between the innovator and the copycat. Microsoft lost out sixfold and they will lose out more and more. They are buying all kinds of firms, but like in the 90’s it is a recipe for disaster and innovators will walk out, they nearly always do. You see, in the end it will bite their bottom line and soon their board of directors will make knee jerk decisions making matters worse. When I stated I would make my IP public domain before I allow Microsoft access to it I was not kidding. Microsoft is as I personally see it becoming the larger problem in any equation and it does not stop there. I made mention of Deeper Machine Learning. This is awesome, it is not AI (AI does not yet exist) but it got me thinking. You see, we now see mention of AI in construction. This is about to go bad, really bad and Trusting these buildings will become folly soon enough. I will try to explain that soon enough. 

The evolution
I looked at the idea before I figured out that there were 600 million bloggers. I have no idea there are on the Vlogger side, but I expect that we are looking at interesting numbers. There are millions of fantasy football fans, hundreds of millions of sports fans and giving them space to expose that idea to them will offer more and more space others would like to try that option. We are in all effect dipping our toes in the water and all these numbers does not mean success, lets be clear about it. My idea remains that, an idea that could be liked by a lot of people, all that considering that others have done close to nothing, makes my idea stellar to say the least. 

When you consider that and when you consider creating ML and DML tools aiding people will create evolution of their work and optionally more people considering this. Not all people are creative, they merely think that their writing is not enough, these tools will enable those on the fence and that is already a win for the exploring team. What matters is that on the end of the weekend I came up with more, all whilst others seemingly came up empty. A nice end to the weekend. I have been considering additions to the field of Vloggers and also places where vloggers can propagate their work. Bloggers have their own space and for that I have additional ideas too. An active field where we switch the awakening to the pro-active, but that is for another day. I did my cerebral activity to keep me happy, time for some Ravioli.

Enjoy Sunday.

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