Interrupt ReQuest (IRQ) is an hardware interrupt on a PC. There are 16 IRQ lines used to signal the CPU that a peripheral event has started or terminated. Except for PCI devices, two devices cannot use the same line. If a new expansion card is preset to the IRQ used by an existing board, one of them must be changed. This was an enormous headache in earlier machines.
Starting with the Intel 286 CPU in 1982, two 8259A controller chips were cascaded together and bumped the IRQs from 8 to 16. However, IRQ 2 is lost because it is used to connect to the second chip. IRQ 9 may be available for general use as most VGA cards do not require an IRQ.
PCI to the Rescue The PCI bus solved the limited IRQ problem, as it allowed IRQs to be shared. For example, if there were only one IRQ left after ISA devices were assigned their required IRQs, all PCI devices could share it.
The problem is more widespread then highlighted in the article. It’s not just these high profile companies using “public domain” images to annotate with facial recognition notes and training machine learning (ML) models. Anyone can scan the Internet for images of people, and build a vast library of faces. These faces can then be used to train ML models. In fact, using public domain images from “the Internet” will cut across multiple data sources, not just Flickr, which increases the sample size, and may improve the model.
The rules around the uses of “Public Domain” image licensing may need to be updated, and possibly a simple solution, add a watermark to any images that do not have permission to be used for facial recognition model training. All image processors may be required to include a preprocessor to detect the watermark in the image, and if found, skip the image from being included in the training of models.
Over the last several months I’ve been researching Quantum Computing (QC) and trying to determine how far we’ve come from the theoretical to the practical implementation. It seems we are in the early commercial prototypical phase.
Practical Application of QC
The most discussed application of Quantum Computing has been to crack encryption. Encrypted data that may take months or years to decipher given our current supercomputing capabilities, may take hours or minutes when the full potential of Quantum Computing has been realized.
Bitcoin and Ethereum Go Boom
One source paraphrased: Once quantum computing is actualized, encryption will be in lockstep progress, and a new cryptology paradigm will be implemented to secure our data. This kind of optimism has no place in the “Real World”. and most certainly not in the world financial markets. Are there hedge funds which rightfully hedge against the cryptocurrency / QC risk paradigm?
Where is the Skepticism?
Is there anyone researching next steps in the evolution of cryptography/encryption, hedging the risk that marketplace encryption will be ready? The lack of fervor in the development of “Quantum Computing Ready” encryption has me speechless. Government organizations like DARPA / SBIR should already be at a conceptual level if not at the prototypical phase with next-generation cryptology.
Too Many Secrets
“Sneakers“, a classic fictional action movie with a fantastic cast, and its plot, a mathematician in secret develops the ultimate code-breaking device, and everyone is out to possess the device. An excellent movie soon to be non-fictional..?
“…companies like Google and Facebook pay top dollar for some really smart people. Only a few hundred souls on Earth have the talent and the training needed to really push the state-of-the-art [AI] forward, and paying for these top minds is a lot like paying for an NFL quarterback. That’s a bottleneck in the continued progress of artificial intelligence. And it’s not the only one. Even the top researchers can’t build these services without trial and error on an enormous scale. To build a deep neural network that cracks the next big AI problem, researchers must first try countless options that don’t work, running each one across dozens and potentially hundreds of machines.”
This article represents a true picture of where we are today for the average consumer and producer of information, and the companies that repurpose information, e.g. in the form of advertisements.
The advancement and current progress of Artificial Intelligence, Machine Learning, analogously paints a picture akin to the 1970s with computers that fill rooms, and accept punch cards as input.
Today’s consumers have mobile computing power that is on par to the whole rooms of the 1970s; however, “more compute power” in a tinier package may not be the path to AI sentience. How AI algorithm models are computed might need to take an alternate approach.
In a classical computation system, a bit would have to be in one state or the other. However quantum mechanics allows the qubit to be in a superposition of both states at the same time, a property which is fundamental to quantum computing.
The construction, and validation of Artificial Intelligence, Machine Learning, algorithm models should be engineered on a Quantum Computing framework.
Amazon, Google, IBM and Microsoft are using high salaries and games pitting humans against computers to try to claim the standard on which all companies will build their A.I. technology.
In this fight — no doubt in its early stages — the big tech companies are engaged in tit-for-tat publicity stunts, circling the same start-ups that could provide the technology pieces they are missing and, perhaps most important, trying to hire the same brains.
For years, tech companies have used man-versus-machine competitions to show they are making progress on A.I. In 1997, an IBM computer beat the chess champion Garry Kasparov. Five years ago, IBM went even further when its Watson system won a three-day match on the television trivia show “Jeopardy!” Today, Watson is the centerpiece of IBM’s A.I. efforts.
Today, only about 1 percent of all software apps have A.I. features, IDC estimates. By 2018, IDC predicts, at least 50 percent of developers will include A.I. features in what they create.
The next “tit-for-tat” publicity stunt should most definitely be a battle with robots, exactly like BattleBots, except…
Use A.I. to consume vast amounts of video footage from previous bot battles, while identifying key elements of bot design that gave a bot the ‘upper hand’. From a human cognition perspective, this exercise may be subjective. The BattleBot scoring process can play a factor in 1) conceiving designs, and 2) defining ‘rules’ of engagement.
Use A.I. to produce BattleBot designs for humans to assemble.
Autonomous battles, bot on bot, based on Artificial Intelligence battle ‘rules’ acquired from the input and analysis of video footage.
IBM Watson’s APIs are available today so teams may ramp up quickly and use IBM’s cognitive computing engine. From IBM Watson’s site, it seems like anyone may build against their cognitive computing platform. In addition, your team may submit to be ‘Featured’ in their application Gallery. Explore the library of featured applications produced by this partnership. At the time of this writing, there were 14 applications.
Several of these apps have been created by IBM to showcase their technology. IBM Watson APIs are categorized into ‘Services’ used:
One of the applications powered by IBM Watson in their gallery is a “News Explorer”, which leverages the Service ‘AlchemyData News’.
The app runs in a browser, and consists of 5 main User Interface components. The centrally placed, “News Network” widget similar to a mind map, correlates articles, companies, organizations, and people. Visually it displays these components and their relationships in groupings similar to a relationship tree.
The left side of the screen has a table called ‘Details’, one column with short descriptions of the stories. From the UI perspective, it enables users to follow the data from left to right, from details to graphical representations.
The right most side of the screen contains a world map leveraged as a heat map in which all the News is derived.
Right under the ‘Locations’ widget, there is a ‘Topics’ tag cloud.
The HoloLens may propel Microsoft back to the ‘Cool’ kid on the block. The HoloLens has the potential to “fly off the shelves” in tandem to Windows 10.
Ever since I saw the movie Blue Thunder, I wanted my own Heads-up display (HUD). Here are a few suggestions for implementation:
Must Have’s
‘Priced to sell’: Even if the cost of the hardware is reduced to a thin the margin, that may or may not be enough. From day one, these “Windows into Windows” must be viewed as essential to the ‘enhanced’ OS package, like ‘Windows 10 Home Media’.
Microsoft, Enable Channel Sales (DELL, HP) to offer Microsoft Windows 10 and HoloLens ‘Media’ package deals, together both the OS and the HoloLens are offered at a reduced price.
The HoloLens and Windows 10 User Interface (UI) significantly enhances how the consumer interacts with Operating Systems.
[Channel] Sales may offer bundled HoloLens / Windows 10 applications, e.g. Minecraft;
Analyze / prioritize top 10 (N) opportunities for application development, and produce internally, and/or with partners.
Partners may range from software vendors to accredited training programs, e.g. flight school XYZ
Intuitive and feature rich developer APIs:
Provide the HoloLens developer a software ‘Simulator’
Products with source code examples
Quick path from development to market: Lean application vetting process; including the vetting of app developers.
Education levels 6 to 12 and beyond can benefit, segmented by: the sciences (e.g. Chemistry, Microbiology, Physics); Trade Schools such as Automotive and HVAC;
Colleges and Universities may be early adopters, and expand their Massive open online courses to including remote participation, e.g. medicine
Applying for an ‘operator license’? HoloLens, accompanied with a licensed operator, allows users to wear the HoloLens, and follow a step by step, interactive tutorial within the vehicle, e.g. Car, Boat, Helicopter, Truck, Airplane, etc.
Anyone ever walk into a room and forget why on Earth you were there? Were you about to get a cup of coffee, or get your car keys? Wonderful! It’s frustrating on my level of distraction, now magnify that to the Nth degree, Alzheimer’s. Apply a rules and Induction engine, and poof! A step further away from a managed care facility.
Teaching the AI Induction and rules engine may require the help of your 10 year old grandson. Relatively easy, you might need your grandson to sleep over for a day or two.
It’s all about variations of the same theme, tag a location, a room in an apartment, also action tag, such as getting a cup of coffee from the kitchen. The repetitive nature of the activities with a location tag draws conclusions based on historical behavior. The more variations of action and coinciding location tags, will begin to become ‘smarter’ about your habitual activities. In addition, the calculations create a bell curve, a way to prioritize the most probable Location/Action tags used for the suggested behavior. The ‘outliers’ on the bell curve will have the lowest probability of occurrence.
Microchip RFID compared to the size of a grain of rice.Beyond this ‘black box’ small, lightweight computer (smartphone) integrate a Bluetooth, NFC, WiFi antenna, a mobile application and you’re set. A small, high quality Bluetooth microphone to interact with the app. There’s also potential for exploring beyond the home.
Kidding, you don’t need that Grandson to help. Speak into the mic, “Train” go into the room and say your activity, coffee. This app will correlate your location, and action. Everyone loves to be included in the Internet of Things, so app features like alerts for deviation from the location ‘map’ are possible.
In earnest, I am mostly certain that this type of solution exists. Barriers to adoption could be computer/ smartphone generational gap. Otherwise, someone is already producing the solution, and I just wasted a bus ride home.
Additionally, this software may be integrated with Apple’s Siri, Google Now, Yahoo Index, Microsoft Cortana, an extension of the Personal Assistant.
Smart Solutions
Definition: Product Owner (PO)
The Product Owner (PO) is a member of the Agile Team responsible for defining Stories and prioritizing the Team Backlog to streamline the execution of program priorities while maintaining the conceptual and technical integrity of the Features or components for the team.
