Factual error....
"Proud owner of Asda"
I doubt anyone would be proud of that.
Retail empire Walmart, proud owner of the British supermarket chain Asda, has appointed Suresh Kumar as its global chief technical officer. Kumar is an old hand at IT, having previously worked for Google, Microsoft and IBM, and spent 15 years in exec roles at Amazon. And of course, Jeff Bezos' behemoth competes with Walmart in …
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Noting his previous logistics background before joining Amazon (i.e. none) it explains a lot. Such as the customer being the only one to notice that a product had gone into a distribution point and never officially left let alone not having left on schedule. Or their system going into confusion if an order didn't get delivered to a locker, treating it as a return and sending a courier to pick up up the non-received item from the customer who hadn't received it without having any notion that sending out a replacement PDQ would be a good idea. Or including a locker over 200 miles away in the nearby location list.
Let's hope they appoint someone who'll take a look at the whole sorry mess.
Fret not. Walmart is bringing in the heavy guns - this Kumar is solid gold (from a golden parachute point of view). He obviously has the high-level view that Fortune 100 CEOs adore so much.
Which obviously means that he has never been a boots-on-the-ground, hands-in-the-mud grunt, oh no, my dear chap. What we have here is the golden-braided horse captain coming in adorned with the glory of many previous campaigns who will, by his mere presence, solve all the issues.
At least, that's what he'll put on his CV when he leaves Walmart for some other high-profile, highly-paid situation. I'm sure he's also 100% buzzword-compliant.
Or being notified for one item arriving on one day of the week, buying a second item so it arrives the same day of the week, getting two delivery attempts for the first item the day before instead of holding it for the day it was supposed to come, along with the second item, then getting notified on the day the first item was supposed to arrive that it may arrive late, followed by a delivery of just the second item...
All of this nonsense is possible because the couriers are paid a pittance.
Please do not forget that Waymo uses my patented AI technology!
- Waymo structures texts by extracting patterns from them,
- Waymo creates synonymous clusters that are analogous to programming language commands, allowing you to find any information in my patented AI database (and, for example, to control robots).
For instance, there is a paragraph
-- Tesla and Waymo are coming. They go fast. --
In this section, there are six patterns:
- Tesla is coming.
- Waymo is coming.
- Tesla goes fast.
- Waymo goes fast.
- They go fast.
- They are coming.
These patterns form three synonymous clusters, around three synonymous nouns. For example one of clusters:
-- Tesla is coming.
-- Tesla goes fast.
Now assigning a certain mechanical action to the pattern "Tesla goes fast" (eg acceleration) and you can control the robot "Tesla driverless car".
The same technology ensures that any information is not only found, but nothing is lost. That is, you can find anything in Walmart's database not by name and keywords, but by meaning in its context, in 100% confidence that nothing will be lost.
Any revolutionary business starts with passion, from a pioneer as the engine of everything. Like, they stole my first patent and what? Google. Google is stealing our secrets, following us everywhere and cooperating with the authorities, because it was created by strangers.
My AI has the potential to make us all free from surveillance and authorities because it replaces Internet with an AI database, which doesn't need our profiles and personal information to function.
And what have they done now? They cut me off from the world, made it impossible for me to start my own AI data mining company.
Any revolutionary business starts with passion, and what passion if the business is started by the hidden from the light thieves? Where's Sergey Brin? Larry Page? Eric Schmidt? Who does the business? Who heads Waymo?
1. Vector search makes it easier to search by concept rather than keyword. For example, if a user types in “How tall is the tower in Paris?” Bing can return a natural language result telling the user the Eiffel Tower is 1,063 feet, even though the word “Eiffel” never appeared in the search query and the word “tall” never appears in the result. Microsoft uses vector search for its own Bing search engine, and the technology is helping Bing better understand the intent behind billions of web searches and find the most relevant result among billions of web pages.
How Walmart can get its hand on this technology? I own it.
2. Synonymous clusters can be indexed relative to one another. For example, the question “How tall is the tower in Paris?” the computer finds the index of the connection with the paragraph which contains the response "the Eiffel Tower is 1,063 feet", even though the word “Eiffel” never appeared in the search query.
4. Walmart can use this, cannot it? For an intelligent taller? For its internal search?
In the 90s search queries were not annotated, not explained. Then came Google which started to explain them by phrases, which it had been finding online, making profiles on people. By adding these phrases Google has been gotten what is known as tuples - in mathematics a tuple is a finite ordered list of elements. This was the idea of my first and stolen patent (PA Advisors v Google).
My new patents came as an answer to NIST TREC QA challenge - how to annotate search queries by both explicit and implicit information, and how to get it. In order to adequately answer to the challenge I invented and patented the new AI-parsing, which opposes the only which had been used for 75 years n-parsing.
But only AI-parsing was not enough. In order to find answers in NIST TREC QA I should build the system how to get the necessary information, which could explain any queries. So, it was necessary to create a fundamentally new relational database, which I did.
Before me only SQL-database exist. For example Oracle, SAP or IBM DB2. My database is different because it doesn't operate with words and phrases (with predetermined manually meanings), but with structured (using the aforementioned AI-parsing) texts.
That is, these structured texts help to explain the questions so that you get answers to them. For example, if a user types in “How tall is the tower in Paris?” Bing can return a natural language result telling the user the Eiffel Tower is 1,063 feet, even though the word “Eiffel” never appeared in the search query and the word “tall” never appears in the result.
Amazon also annotates pictures with structured texts and looks for both graphic images and structured texts. Walmart doesn't do this.
Dictionary plays a crucial role in data structuring. Structured dictionary definitions explain texts to computer by creating tuples; where in mathematics a tuple is the final ordered list of elements. By comparing a word's tuple with its surrounding text can be found out what part of speech the word belongs to, and uniquely index it by this definition.
For example, the definition of the word "red":
- adjective
color end of the spectrum next to orange and opposite violet, as of blood, fire, or rubies.
"her red lips"
synonyms: scarlet, scarlet, ruby, ruby-red, ruby-colored, cherry, cherry-red, cherry, cherry, cardinal, Carmine, wine, wine-red, wine-colored, Burgundy, Burgundy-red, Burgundy, blood-red;
- noun
1. red or pigment.
"their work is marked by a red teacher"
2. red thing.
"which dress is black or red?"
Each word, each synonym, each example in this definition has its own definition, which allows the building of a unique tuple for the word "red", and match it with the surrounding text. For example, the word "scarlet" allows you to deepen the tuple, to explain it in more detail:
- adjective
1: scarlet color
2 : grossly and glaringly offensive
sin, clear and scarlet.
Or the word "spectrum":
- noun
1. a band of colors, as seen in a rainbow, produced by separation of the components of light by their different degrees of refraction according to wavelength.
2. used to classify something, or suggest that it can be classified, in terms of its position on a scale between two extreme or opposite points.
Using all structured definitions is possible to create a huge descriptive text for any pattern, which allows computer to found it instantly. Indeed, such a text for one patterns could be 1-10.000 patterns long. And imagine how big could be a structured description for a set of 50 patterns? 500.000 patterns!
For example, Walmart decided to sell red-scarlet bananas? And an employee of Walmart wrote: “we are starting to sell red-scarlet bananas with incredibly good taste!” The computer immediately structures this description, automatically creating and adding new patterns. And this expanded description will be 100,000 significant patterns, although the Walmart employee has entered only 10 words describing the product.
This very long set of patterns allows the access to the bananas to the right customers.