Introduction

1.This document contains a set of scenarios concerning inventions that involve artificial intelligence (AI) or machine learning (ML). It is designed to accompany the guidelines for examining patent applications relating to AI inventions. The guidelines are primarily concerned with the patentability of AI inventions in respect of the excluded matter provisions set out in section 1(2) of the Patents Act 1977.

2.The IPO considers that patents are available for AI inventions in all fields of technology. The scenarios in this document are intended to reflect and illustrate the wide range of diverse technical fields where AI inventions may be found.

3.Each scenario has a very brief description of how its AI invention works and an illustrative example of a patent claim. Each scenario includes a simplified assessment setting out our opinion on how each AI invention would likely be assessed in respect of section 1(2).

4.For the avoidance of doubt, we emphasise that this document is not a source of law. Our opinions on the patentability of the scenarios shall not be binding for any purpose under the Patents Act 1977.

5.The scenarios have been designed to focus on the question of excluded matter only. We have assumed that the claimed inventions are novel and non-obvious. We have also assumed that each scenario is sufficiently disclosed.

6.The assessments of excluded matter we give follow a simplified application of the four-step Aerotel approach. We have omitted detailed consideration of steps 1 and 2 of the Aerotel approach. Specifically: a. At step 1, we have simply assumed each claim is sufficiently clear and that no issues of construction arise. b. At step 2, we have simplified the assessment by stating what we consider is the actual contribution c. At steps 3 & 4, we have simplified the analysis by focussing in the main on the program for a computer exclusion of section 1(2). Unless otherwise indicated, our non-binding opinions are limited to this exclusion.

7.Any comments or questions arising from these scenarios should be addressed to:

Phil ThorpeIntellectual Property OfficeConcept HouseCardiff RoadNewportSouth WalesNP10 8QQ

(Telephone 01633 813745)Email: Phil Thorpe

Nigel HanleyIntellectual Property OfficeConcept HouseCardiff RoadNewportSouth WalesNP10 8QQ

Telephone 01633 814746Email: Nigel Hanley

Background This invention concerns a parking management system located in a car parking facility equipped with camera surveillance.

Images from the systems cameras are processed by a first neural network which is trained to detect a vehicle approaching an entrance of the facility. When the first neural network detects an approaching vehicle in an image, the image is passed to a second neural network to implement an Automatic Number Plate Recognition System (ANPR) system. The second neural network is trained to identify a specific number plate region in the image. A recognition module receives the identified number plate region and applies an optical character recognition algorithm to determine the registration number of the vehicle.

A number plate recognition system comprising:

an image capturing device positioned at an entrance to a parking facility;

a computing device for receiving images from the image capturing device and comprising:

a first neural network configured to detect a vehicle in a captured image;

a second neural network configured to receive an indication of the vehicle from the first neural network, detect the presence of a number plate in the image, and determine a region of interest in which the number plate is located; and

a recognition module configured to receive the region of interest and apply an optical character recognition process to the region of interest to determine characters of a registration number of the vehicle.

The contribution

Beyond a conventional surveillance system having a camera and a computer, the contribution made by the invention is:

a number plate recognition system using a first neural network to detect a vehicle in a captured image; a second neural network to detect the presence of a number plate in the image and to determine a region of interest in which the number plate is located, and a module to apply an optical character recognition process on the region of interest to determine the characters of the vehicles registration number.

The contribution is a number plate recognition system which is not excluded by section 1(2). Although the number plate recognition system is computer implemented, it is more than a program for a computer as such because it is carrying out a technical process external to a computer. The number plate recognition system includes a combination of two neural networks and a recognition module that specifically perform image processing operations which are technical in nature (see Vicom). The system has a technical effect in the sense of signpost (i). It self-evidently solves a technical problem relating to the recognition of vehicle registration plates, so signpost (v) would also point to allowability.

The claimed invention is not excluded.

Gas supply systems are complex systems that are monitored by multiple sensors located in the supply system and in its operating environment. Typically, data from sensors can be combined and analysed by an operator to provide the operator with an overview of the operational state, both of the individual components within the system and of the system as a whole. This may assist the operator in identifying faults within the system and options for reconfiguring the system.

However, it is acknowledged that this approach requires specialised skills on the part of the operator and is prone to error, especially when data from large numbers of interconnected sensors must be considered. In particular, understanding the interdependency of changes made to the system is challenging.

This problem has been recognised by the inventor, who has developed an artificial intelligence system to receive and categorise sensor data relating to a gas supply system, identify faults, and recommend system configuration changes to resolve the faults. In making recommendations the AI system will analyse the effect a configuration change may have on the system. The system may implement a recommended configurational change to the system using an automatic operational controller.

A computer-implemented method of managing the operating state of a gas supply system using sensors within the gas supply system and in its operating environment, and characterised in that the method comprises an AI system:

receiving and analysing data from the sensors;

identifying fault conditions within the gas supply system based on the analysis;

and

reporting the fault conditions and generating a recommended solution to an automated operational controller of the gas supply system.

The contribution

The contribution is managing the state of a gas supply system using an AI system that identifies fault conditions in the gas supply system, based on sensor data relating to the operation of the gas supply system, and reports the fault conditions and recommended solutions to an automated operational controller.

The contribution does not fall solely within the computer program exclusion. The contribution made by the invention is a solution to a technical problem lying external to the computer on which the AI system runs, namely the monitoring of the operation of an external technical system (a gas supply system) for fault conditions. This is a technical contribution. Signposts (i) and (v) point to allowability.

The invention defined in the claim is not excluded under section 1(2).

Analysing the motion of an object can be used to identify an activity. In some known examples, such as sporting events, analysing motion of an object can be useful for coaching. Alternatively, in gesture-based systems, a determined gesture can be used as a control mechanism or to issue an alarm. In one known example, a smoking cessation system generates an alarm in a wrist-worn device to deter the user from smoking.

Typically, these known systems operate by comparing real-time data to statistical models to determine motion, and they are heavily reliant on the accuracy of their statistical model. Consequently, systems that rely on the statistical models can be inaccurate.

The inventor has proposed a system that uses motion vectors derived from acceleration, velocity, and orientation in X, Y and Z directions as an input to a neural network to classify the motion. The system functions by receiving motion data in real time from a device such as a sports watch, or other motion sensors. The neural network processes the motion vector using a classification library to classify the motion to a particular movement.

A computer-implemented device for analysing motion comprising:

a controller having a data interface, a neural network, and a movement classification library;

sensors including a gyroscope, a magnetometer, and an accelerometer, wherein data from each sensor each is output to the controller via the data interface;

characterised in that the controller is operable to:

determine a motion vector from the received data; and

provide the determined motion vector to the neural network, wherein the neural network is configured to classify the motion vector as one of a particular movement in the classification library.

The contribution

The contribution is a device that determines a motion vector from data captured by its sensors (gyroscope, magnetometer, accelerometer) and uses a neural network and classification library to classify the motion vector as a movement from the library.

The contribution is not solely a program for a computer since its task is to perform a process of classifying measured sensor data describing the physical motion of a computing device. This process is a technical process lying outside the computing device and is carried out by technical means. It concerns the classification of real-world sensor data as a determined movement. Signpost (i) would point to patentability.

The invention defined in the claim is not excluded under section 1(2).

Cavitation in a pumping system is the formation of vapour bubbles in the inlet flow region of the pump, which can cause accelerated wear, and mechanical damage to pump seals, bearings and other pump components, mechanical couplings, gear trains, and motor components.

A pump system has a measuring apparatus adapted to measure pump flow and pressure data associated with the pump system. A classifier system detects pump cavitation according to the flow and pressure data. The classifier system comprises a neural network which is trained using back propagation. The measuring devices comprise sensors (1,2) for measuring input pressure and output pressure associated with an inlet (3) and an outlet (4), respectively, of the pump system. The flow through the pump is also measured.

1.A method of training a neural network classifier system to detect cavitation in a pump system, the method including the steps of:

correlating each of a plurality of measured pump flow and pressure data pairs with one of a plurality of class values, thereby producing a training data set, wherein each of the plurality class values is indicative of an extent of cavitation within the pumping system and at least one of the plurality of class values is indicative of no cavitation in the pump system; and

training the neural network classifier system using the training data set and back propagation.

2.A method for detecting cavitation in a pump system comprising:

measuring pump flow and pressure data;

detecting pump cavitation according to said flow and pressure data;

wherein the detection step includes providing said flow and pressure data as inputs to a classifier system using a trained neural network, wherein the neural network provides a signal indicative of the existence and extent of cavitation in the pump system, and updating said signal (6) during operation of said pump system.

The contribution

Beginning with claim 1, the contribution it makes is a computer-implemented method of training (i.e. setting up) a neural network classifier so it can detect cavitation in a pump system, where the method uses back propagation with a set of training data comprising measurements of pump flow and pressure from the pump system that are each correlated with a value indicating a corresponding extent of pump cavitation in the system.

Although the contribution relies on a computer program, it is more than a computer program as such. The contribution relates to a process of using physical data to train a classifier for a specific technical purpose, namely the detection of cavitation in a pump system. This is technical in nature. There is a technical contribution in the sense of signpost (i).

Claim 2 also reveals a technical contribution since it relates to the use of a trained classifier for the specific technical purpose of detecting cavitation in the pump system. A technical process lying outside the computer in the sense of signpost (i) is performed.

The invention defined in claims 1 and 2 is not excluded under section 1(2).

Many cars are fitted with catalytic converters to reduce the amounts of gases such as NOx and CO in their exhaust fumes. A problem for such converters is that their operational efficiency changes with the ratio of fuel-to-air in the combustion chambers of the engine. The fuel-to-air ratio must therefore be controlled to be maintained at a fixed value to maintain the efficient operation of the catalytic converter.

It is known to control the amount of fuel injected into an engines combustion chamber using feed forward control in relation to throttle position and additional feedback control in relation to an oxygen sensor (or air/fuel sensor) provided in the exhaust. Although this works well, it can be difficult to control the fuel-to-air ratio correctly when the engine is accelerating or decelerating.

The inventor has developed an injector control system which uses a trained neural network to determine an amount by which a given fuel injection amount should be adjusted during acceleration/deceleration to maintain a correct fuel-to-air ratio and thus maintain catalytic converteor efficiency. The neural network receives data inputs relating to the operational state of the engine, such as engine speed (RPM), intake air pressure, throttle position, fuel injection amount, air intake temperature, engine coolant temperature, and data from an exhaust gas sensor. The neural network outputs a signal indicating a change to the fuel injection amount for controlling the engine.

A computer-implemented neural network for adjusting the amount of fuel injected into a cylinder of a combustion engine, the neural network comprising:

an input layer having:

an input for receiving the RPM of the engine;

an input for receiving intake air pressure of the engine;

an input to receive current throttle position;

an input to receive the present injected fuel amount;

an input to receive air intake temperature;

an input to receive water cooling temperature;

an input to receive exhaust gas sensor data;

at least one hidden layer, wherein the hidden layer is connected to the input layer;

an output layer connected to the at least one hidden layer; and wherein

the output layer has an output indicating an amount by which the fuel injection should be changed.

The contribution

The contribution is a neural network that outputs a control signal relating to an amount by which fuel injection should be changed based on inputs relating to the operational state of the engine, as defined in the claim.

The contribution is a solution to a technical problem lying outside a computer, i.e. maintaining correct fuel-to-air ratio in an engine, so it is more than a program for a computer as such. The neural network takes as its inputs data representing the operating state of the engine and outputs a control signal indicating the amount by which a fuel injection amount should change. The control signal is suitable for controlling a technical process that exists outside of the computer on which the neural network runs. This is a technical contribution. Signposts (i) and (v) apply.

The invention defined in the claim is not excluded under section 1(2).

It is useful to measure the percentage of blood leaving each ventricle of a heart when determining the health of the heart. This measurement can be estimated by a skilled operator of an ultrasound imaging system by imaging a heart and marking out and measuring the boundaries of the ventricles of the heart at either extreme of a heartbeat. However, the accuracy of the operators estimate depends upon the operators skill and judgement.

The inventor has devised a method in which a trained neural network is used to provide a measurement of the percentage of blood ejected by a heart by analysing a series of images of the heart over a heartbeat. The neural network is trained using a supervised learning approach.

A computer-implemented method for determining a percentage of blood ejected from a given heart during a heartbeat, the method comprising:

training a neural network with heart imaging data sets, each set comprising imaging data of a ventricle over time and associated blood ejection percentages, the sets being associated with different hearts;

and using the trained neural network to:

receive a set of imaging data of a ventricle of the given heart;

output a percentage of blood ejection for the given heart.

The contribution

The contribution is a method of estimating a percentage of blood ejected from a heart by training a neural network with heart imaging data which has been labelled with blood ejection percentage, and then obtaining an estimate for the percentage of blood ejected from a given heart over a heartbeat by providing a set of images of that heart (over its heartbeat) to the trained neural network.

The contribution is more than a program for a computer as such because it relates to an improved measurement of the percentage of blood ejected from a heart during a heartbeat. This is a technical measurement of a physical system. This improved measurement is an example of a technical effect upon a process lying outside the computer that implements the invention, following signpost (i). This is a technical contribution.

The invention is not excluded under section 1(2).

Traders on a trading exchange monitor the performance of various stocks and tradeable instruments to try to identify opportunities to make a beneficial trade. It requires specialist knowledge, understanding, and experience to recognise and identify patterns and trends in the market. This means traders will often specialise in a narrow range of instruments e.g. energy shares, financial derivatives, or commodities.

The inventor has recognised that this can result in beneficial trades being overlooked. A trader may either miss a trading opportunity for instruments held as part of their position or a chance to a reduce a loss or increase a profit from a transaction. To assist the trader, the inventor has developed an AI that can identify patterns and correlations between share and instrument prices, identify trades based on recent performance and timing differences, and predict future behaviours. One advantage the AI offers is the opportunity to see connections that would be otherwise opaque and not obvious.

The AI is coupled to an automatic brokerage platform to allow it to execute trades according to profit/loss limits provided by the trader.

A computer-implemented financial instrument trading system comprising an exchange market, a broker terminal, an AI assistant, and an automated brokerage system, characterised in that the AI assistant is configured to:

receive current and historical price data for tradeable financial instruments from the exchanges;

cross reference combinations of financial instruments to identify correlated groups of instruments;

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Examining patent applications relating to artificial intelligence (AI) inventions: The Scenarios - GOV.UK

LONDON, Sept. 27, 2022 (GLOBE NEWSWIRE) -- According to The Business Research Companys research report on the high energy lasers market, the emergence of artificial intelligence is gaining popularity among the high energy lasers industry trends. Many companies operating in the market are focused on developing AI-based products to get a competitive advantage.

For instance, in April 2022, the US Navy successfully tested the Layered Laser Defense (LLD), a laser weapon designed and developed by Lockheed Martin, a US-based aerospace, arms, defense, information security, and technology company. This is the Layered Laser Defense (LLD). It can use a high-power laser to counter unmanned aerial systems and fast-attack boats, as well as track inbound air threats, support combat identification, and conduct battle damage assessments of engaged targets. With specialized optics for viewing a target and directing laser beams for maximum effect, as well as artificial intelligence to improve tracking and aiming, LLD is compact and powerful yet more efficient than previous systems.

Request for a sample of the global high energy lasers market report

The global high energy lasers market size is expected to grow from $10.42 billion in 2021 to $12.05 billion in 2022 at a compound annual growth rate (CAGR) of 15.7%. The global high energy lasers market growth is expected to reach $19.97 billion in 2026 at a CAGR of 13.5%.

The rising demand for laser weapon systems in defense is expected to propel the growth of the high-energy laser market going forward. Laser weapons systems are described as systems that solve recognized capability gaps against asymmetric threats (UAS [unmanned aerial systems], small boats, and ISR sensors). Laser weapon systems are highly necessary in the Navy and Air Force across the world to prevent airborne threats like missiles and drones, as lasers are effective against missiles and are being used as the first line of defense. For instance, in 2019, Northrop Grumman Corporation, a US-based aerospace and defense company, and the United States Air Force Research Laboratory (AFRL), inked a contract to develop an advanced laser system. The laser system has been used to defend US fighter jets against enemy missiles. Apart from that, in 2021, the DRDO (Defense Research and Development Organization), an India-based government agency aimed at the production of a high-power laser weapon, is eyeing a budget of $100 million from the ministry of defense. As a result, the market, which was valued at $7.4 billion in 2020, is expected to reach $14.7 billion by 2026. Therefore, rising demand for laser weapon systems in defense is driving the growth of the high energy market.

Major players in the high energy lasers market are SICK AG, Petasense Inc, Allegro MicroSystems, Inc., Robert Bosch GmbH, NXP Semiconductors NV, Infineon Technologies AG, Sensoronix, Inc., TE Connectivity, Inc., SPECTEC, Sensor Solutions Corporation, ABB, Rockwell Automation Inc., STMicroelectronics, Pepperl+Fuchs, and Schneider Electric.

The global high energy lasers market is segmented by product type into gas laser, fiber laser, solid state laser, excime laser, and by application into cutting, welding, and drilling, military and defence, and communications.

North America was the largest region in the high energy lasers market in 2021. Asia-Pacific is expected to be the fastest-growing region in the high energy lasers market during the forecast period. The regions covered in the global high energy lasers market research report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, the Middle East, and Africa.

High Energy Lasers Global Market Report 2022 Market Size, Trends, And Global Forecast 2022-2026 is one of a series of new reports from The Business Research Company that provide high energy lasers market overviews, analyze and forecast market size and growth for the whole market, high energy lasers market segments and geographies, high energy lasers market trends, high energy lasers market drivers, high energy lasers market restraints, high energy lasers market leading competitors revenues, profiles and market shares in over 1,000 industry reports, covering over 2,500 market segments and 60 geographies.

The report also gives in-depth analysis of the impact of COVID-19 on the market. The reports draw on 150,000 datasets, extensive secondary research, and exclusive insights from interviews with industry leaders. A highly experienced and expert team of analysts and modelers provides market analysis and forecasts. The reports identify top countries and segments for opportunities and strategies based on market trends and leading competitors approaches.

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Laser Weapon Systems Market 2022 By Product (Laser Designator, Lidar, 3d Laser Scanning, Laser Range Finder, Ring Laser Gyro, Laser Altimeter), By Technology (Solid State Laser, Chemical Laser, Free Electron Laser, Chemical Oxygen Iodine Laser, Tactical High Energy Laser, Others), By Application (Air Based, Ground Based, Sea Based), And By Region, Opportunities And Strategies Global Forecast To 2031

Laser Marking Machine Global Market Report 2022 By Offering (Hardware, Software, Services), By Type (CO2 Laser, Fiber Laser, Green Laser, UV Laser, YAG Laser, Others), By Application (Automotive, Aerospace, Machine Tool, Electronics And Microelectronics, Medical, Packaging, Military, Others) Market Size, Trends, And Global Forecast 2022-2026

Laser Cutting Machine Global Market Report 2022 By Technology (Solid State, Gas, Semiconductor), By Process (Fusion Cutting, Flame Cutting, Sublimation Cutting), By End User Industry (Automotive, Consumer Electronics, Defense And Aerospace, Industrial, Other End User Industries) Market Size, Trends, And Global Forecast 2022-2026

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The Emergence Of Artificial Intelligence Is A Key Trend In The High Energy Lasers Market As Per The Business Research Company's High Energy Lasers...

The COVID-19 pandemic has not only changed how we interact but also how we do business. At the start of 2020, almost all sectors were forced to operate from home, a scale unimaginable in the 21st century. Naturally, it forced companies to think out of the box and come up with ways for talent retention and acquisition. There have been a lot of talks about incorporating AI into the Human Resource Department (HRD) to look for the ideal candidate and to check on employees' well-being.

While that reality is still a dream, some are looking for ways to upgrade their Human Resource department, especially in this hybrid work environment.

To learn more about the future of talent acquisition and retention, India Today Tech spoke to Deepti Varma, Vice President, People and Experience Technology (PXT) at Amazon Stores India and EM. The senior Amazon executive also spoke about the role of technology in providing a better employee experience.

During the conversation, Varma even clarified that Amazon brands its HR department as PXT (People Experience and Technology Solutions). That's because the company does a "hypothesis across talent acquisition, talent retention, talent development" and then looks at data to find the best solution.

In 2022, how much relevance do you think traditional CVs and online profiles possess during a hiring process?

At Amazon, more than just the skills, we believe that we need to focus on whether the person is culturally fitting into the company. So, even before we start trying to look at skill, we first check whether the person would be able to align themselves to the culture. So, the traditional CV can do only a little justice with what the person would have to offer. I think there is a lot that we gather through a very intensive interview process that we have, and we kind of rely on that a lot.

Can you elaborate a bit on this process? Are you relying on technology or some kind of tool?

We use a lot of AI and have quite a lot of automated tools that we use in order to hire. In fact, as we speak, in the US, we are experimenting with 'no see hire', which means that without actually asking the person to come for technical roles, can we look at hiring people based on some of the simulations that we have made, and can we, try to reduce the dependencies on a recruiter. So we are trying to do a lot of these things, in fact, because the number of people that we hire is so many, we don't believe in a traditional way of scheduling interviews. All we use a tool to do that [hire] where the candidate as well as the person who's interviewing, their schedule gets matched and there is a schedule that gets popped up on its own.

Do you think a maximum dependency on tech like AI is ideal for this segment of business?

I am going to give my view and, it can be very different from what industry experts feel.

The short answer to your question is that both algorithms, as well as human decisions, can be flawed. It cannot be either or it cannot be too much usage of technology or too much usage of human intervention that would help us to scale in the future. But I feel technology certainly would be a great enabler for us to focus on the things that can be simplified in order for us to scale talent acquisition in the future.

And I'll give you a very simple example of that. Today, when you look at an Amazon.in website, no one goes around training you, this is how you need to show up at Amazon. So, the way we have used the technology is, that it should be simple and scalable. It should be intuitive, and you don't need to get trained on it. The way we want to use technology is that it should be simple, scalable, should have a good candidate experience because, for us, we always work backwards from the customer's need, but it should be something that should be an enabler rather than being something that we solely depend on.

Can you elaborate on some tools and techniques that Amazon uses for acquisition, data retention, and development?

I would start with something that we actually experimented during COVID time. We launched something called Alexa onboarding. We used Alexa to onboard our employees. And Alexa would answer a lot of questions. Because people wanted to understand about Amazon's culture, people wanted to understand about how should they navigate the ways in first 30 days. In fact, we got an Economic Times award for the experiment.

Then, during COVID times, we were hiring from campus, and we were hiring quite a lot. We thought, are we missing out on women-talent, so we launched something called as 'Amazon Wow', where we were trying to hire through technology. And this is a technology portal that we started where women, students from all across India, engineering college could apply.

There is one more thing which is very close to my heart, which I am sure you would have heard about is 'Amazon Connections'. And what Connection basically does is that as soon as anyone across the globe open their laptop early in the morning, the first thing - it just pops a question onto your screen to understand how you are feeling. Amazon believes that you should be checking how the employees feeling throughout the year. So, every day there is one question that gets asked to you on different aspects that are important.

No technology is perfect. How do you think Amazon can then evolve these tools that it is using?

If a technology can be used to proactively like if machine learning can be used to proactively assess if the person is having stress or may be going towards a situation where they could experience stress and you can proactively, just take care of that employee that would be more meaningful. And I don't think we've reached that stage. But

The vision is such that we would want to use technology to proactively predict what's coming. And how do we help the employee in order to handle that situation? So that is something we haven't yet been able to crack. But that is something that is one of the visions that we have that we should see. There are other places where we feel we could use technology more. This is just one example that I wanted to share.

(Conversation with Deepti Varma has been edited for clarity)

--- ENDS ---

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Can artificial intelligence replace HR department? Amazon on future of hiring and talent retention - India Today

DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence Chip Market by Chip Type, by Application, by Architecture, by Processing Type, by End User - Global Opportunity Analysis and Industry Forecast, 2022-2030" report has been added to ResearchAndMarkets.com's offering.

The Global Artificial Intelligence (AI) Chip Market size was valued to USD 20.77 billion in 2021, and it will elevate to USD 304.09 billion by 2030, with a CAGR of 29.9% from 2022-2030.

Artificial Intelligence Chips are special silicon chips, programmed for machine learning. AI Chips can process vast amount of data, identify the underlying patterns, interpret the trends and utilize the feed to achieve specific goals. AI Chips are multi-functioning and can proficiently manage multiple operations at a time.

The demand for AI Chip is consistently raising due to the increasing adoption of artificial intelligence in almost every industry, in areas such as voice recognition, object detection, medical or military simulation, intelligent routing, and autonomous driving among others. Adoption of artificial intelligence not only reduces the cost of operations, but increases the efficiency or the response time and minimizes the risk to human life at various levels in specific industry verticals.

Market Dynamics and Trends

With the technological advancements the market is switching towards smart devices, smart homes, and smart cities, resulting to a tremendous elevation in the Artificial Intelligence (AI) Chip market. Furthermore, increased investments in AI start-ups along with emergence of quantum computers, are expected to elevate the market growth in future.

Other factors like wide application of AI technology and increase in robotics, catalyze to promote the market growth. However, high development cost and lack of skilled work-force tend to impede the growth of artificial intelligence (AI) chip market over the forecast period.

Moreover, the massive upsurge in research and development, increased use of autonomous robotic at various industry verticals, and high-tech product launches shall create new market-opportunities, fueling-up the growth-rate of artificial intelligence (AI) chip market, over the forecast period.

Companies Mentioned

Key Market Segments:

Global Artificial Intelligence (AI) Chip market - By Chip Type

Global Artificial Intelligence (AI) Chip market - By Application

Global Artificial Intelligence (AI) Chip market - By Architecture

Global Artificial Intelligence (AI) Chip market - By Processing Type

Global Artificial Intelligence (AI) Chip market - By End User

Global Artificial Intelligence (AI) Chip market - By Geography

For more information about this report visit https://www.researchandmarkets.com/r/508z6

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Global Artificial Intelligence Chip Market Report to 2030 - Increasing Focus on Human-Aware AI Systems and Development of Smarter Robots Presents...

The modern invention of drones and other high-tech products powered by AI technology can be great for some but an absolute nuisance for others. And firms getting harmed by them can now breathe a sigh of relief thanks to new EU Draft rules.

As reported by Reuters, its going to be much easier to get the compensation firms to feel they deserve from damage done through these devices.

By Wednesday, well be witnessing a few directives come our way via the European Commission. This is being done to mark the growing rage of AI-powered technology and to better provide the necessary guidance needed across 27 different nations of the EU.

Those trapped or victimized may put their cases forward in terms of damage done to their lives, health, privacy, and their property. This might be due to reasons like faults carried out by providers during the recruitment process, as delineated in the draft rules.

The regulations are outlining the huge amount of proof seen on victims by putting forward a term called presumption of causality. This means victims will only be required to put forward a reason that shows how a manufacturer failed to stay in line with certain rules and cause harm. Then, this would be linked to AI technology in the case.

Under the right to gain access to the cases evidence, all victims may request the court to force companies to give more information about high-risk AI systems. This way, they can better identify who is responsible and what really went wrong.

In the same way, well be seeing the executive provide updates to its directives linked to Product Liability. This sets the tone for all products that are defective and arise from smart technology to pharmaceuticals and even machines.

These new changes are going to provide users with the right to sue whenever a software update possesses the likelihood of harm to their devices or even when producers arent able to amend security gaps.

However, those having dangerous products that are non-EU based wont have this right under the new system of compensation.

For now, the new directive needs to be issued a green light from various countries in the European Union before it can turn into law.

But just the thought of being compensated for the harm done through such technology has many companies excited.

Continue reading here:
Companies Suffering Harm From Drones And Artificial Intelligence Products Can Now Sue Under EU Draft Rules - Digital Information World

By Richard Johnstone on 28/09/2022 | Updated on 28/09/2022

Image by AkshayaPatra Foundation from Pixabay

The United Nations education agency has called on the Indian government to take a host of actions to boost public trust and understanding in the use of artificial intelligence in education.

In its annual report on thestate of education in the worlds largest democracy, The United Nations Educational, Scientific and Cultural Organization (UNESCO) focused on the potential role for AI in schools. Indias 2020 National Education Policy has called for increased technical knowledge at all levels of education, including the integration of AI to promote quality and skill-based education.

UNESCO said that the use of AI had endured various misconceptions, and highlighted that AI could have a number of positive uses in the Indian education system. These included using intelligent tutoring systems to conduct assessments, track learning progress and provide regular individual feedback, and the development of smart schools and universities to deliver quality content to remote locations using AI techniques like facial and speech recognition alongside augmented and virtual reality.

However, its recommendations called for an increased focus on improving trust in artificial intelligence. In particular, the review called on the government to consider the ethics of artificial intelligence in education as an utmost priority as well as rapidly providing an overall regulatory framework for artificial intelligence in education.

Read more: Artificial intelligence in the public sector: an engine for innovation in government if we get it right

Other recommendations in the report included a need to improve public trust in artificial intelligence, with options including more work by government to expand AI literacy efforts, and attempting to correct algorithmic biases and the resulting discrimination.

The recommendations come as the Indian government aims to improve its digitisation in a host of areas through its Digital India programme, which is intended to transform the country into a digitally empowered society and knowledge economy.

Eric Falt, the director of UNESCOs New Delhi office, said that while improving the quality of education and the learning outcomes of students are the utmost priorities of all countries, India has made significant strides in its education system.

Strong indicators point to the countrys notable efforts to enhance learning outcomes, including by using artificial intelligence-powered education technology, he said.

Read more: Canada to create top official to police artificial intelligence under new data law

Dr. Rajendra Kumar, the additional secretary in Indias Ministry of Electronics and Information Technology, said the ministry was really pleased to note that UNESCO has dedicated the theme of its 2022 State of the Education Report for India to artificial intelligence in education, adding: We believe that this report is timely, as it can contribute towards transforming India into a digitally empowered society and knowledge economy and make Digital India a reality.

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Indian government must address AI ethics in education 'as utmost priority' - Global Government Forum

There has never been a better time to be a CIO. In todays challenging macro-economic environment with high inflation, constrained supply chains, talent shortages and hybrid work among other challenges the technology strategy drives the business strategy. Hence, the role of the CIO is essential to every organisations success.

With companies facing multiple challenges in todays economy, its crucial they uphold customer loyalty at a time when so many other uncertainties threaten business growth. CIOs will need to make the most of emerging technologies in the enterprise, such as artificial intelligence (AI), to maintain loyalty and enhance the customer journey especially when consumer expectations remain high.

One focus area is utilising AI to enable account teams to make more informed decisions. Customer-facing staff have traditionally relied on past experiences and instincts to help manage expectations. However, connected AI models can make predictions to power decision-making capabilities internally, and these powerful insights deliver value for clients and employees alike. For instance, a decision-assist approach uses AI to give staff recommendations on how to optimise each customers user experience.

Lets say a business hires a new account executive in the sales team and they manage some of the key accounts. At any point in time, there are hundreds of actions they could perform on the customer to change the trajectory of the relationship. For example, they could fix an implementation issue, suggest tactics to increase product usage, encourage the client to take a training course, or fix a support issue.

An AI system will be the repository of things that happened in the past, things happening right now, and what could happenin the future

By creating an AI system, a company can subsequently amass the customers marketing, sales, success, product, and support interactions with their business. These interactions are organised, connected and enriched in the sales system. Companies can then run various action scenarios for every customer to figure out the best actions to yield a better relationship. It learns from the past successful actions based on the results and prioritises it for the future. In this way, the system learns from the best and scales everyone to be the best.

An AI system will learn from real-time feedback on the recommendations and the actions taken towards each customer. It will be the repository of things that happened in the past, things happening right now, and what could happen in the future.

A growing number of companies are realising that AI can also help reduce customer churn, a significant risk for any business. Preventable churn is when a company can intervene when the risk is known in advance and issues can be avoided. By analysing why certain client relationships have broken down and contrasting that with successful customer journeys, businesses can better understand the reasons for churn.

These reasons include product usage, customer experience, feedback, engagement and training data. When a new customer exhibits these risk factors, an AI model can assign the churn a risk score, which is computed for all the clients. Those with the highest risk scores are then used for intervention actions based on the specific risk factors to mitigate the churn.

This complements ERP as every action a company takes to improve its customer experience is an investment. Many of these actions consume resources and must be planned accordingly. AI-driven actions are not only creating positive value for clients but also increasing loyalty and driving business revenue. This shows how important technology is to a companys business strategy and proves theres never been a better time to be a CIO.

Chris Bedi is chief digital information officer, ServiceNow

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Artificial intelligence will revolutionise the customer journey ERP Today - ERP Today

Opinion: AI can now help write essays that appear highly similar, if not indistinguishable, from that of a human author

By Eamon Costello and Mark Brown, DCU

Artificial intelligence-powered automatic essay writing technologies have taken a huge leap forward and are becoming widely available. In many cases, AI can help write essays that appear highly similar, if not indistinguishable, from that of a human author.

As this piece was being typed, Google AI helpfully suggested words to auto-complete these sentences. This speeds up writing but other tools and services, such as those based on the GPT-3 technology from the OpenAI group, can do much more than simple sentence completion: they can write the whole thing from scratch from start to finish.

AIs can generate surprising comprehensible text

By simply taking a prompt of a sentence or phrase from a human author, AIs can generate surprising comprehensible text. When the prompt "when an AI wrote my essay" was typed into a freely available online AI text generator to see what it would produce, the AI wrote: the following

"And don't forget", the professor said, "AIs are not allowed to write your essay or help you write your essay in any way" I groaned and rolled my eyes, thinking that I knew what he was going to say. I had never been good at writing essays, and I knew that an AI would only make it worse. But when I sat down to start it, I found myself in the middle of a beautiful essay. The words flowed easily from my fingers, and I was suddenly inspired. I had never written so well, and I was surprised that the AI had written it for me. I finished the essay and handed it in, feeling proud of myself. I didn't know what the professor would say, but I was sure that I had done a better job than if I had written it myself.

One can see how a student could generate text in this way, perhaps generating several texts, seeing which one suited best and then lightly editing for clarity. AI can also take large bodies of text as input and attempt to summarize them.

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From RT Radio 1's Today with Claire Byrne, Brian O'Connell reports on student essay mills

A student could prompt an AI to write several paragraphs and could then select which paragraphs seemed most well written and coherent. Finally, they could feed these paragraphs back to the AI asking it to summarise them. This could form the basis of a summary/conclusion section of an essay.

Using AI in this way requires particular skills. Just as correct spelling is becoming less critical with the ability of autocorrect, perhaps essay writing will evolve similarly. It may be that writers in the future engage in the higher level activity of orchestrating a composition, while AI does the heavy lifting of producing the actual sentences.

If using a spell checker isn't seen as cheating, will the use of AI for essay writing then be accepted as the new normal? Perhaps future students will use AIs to write their essays, while professors deploy AIs to check their authenticity.

Of course AIs can grade essays too, but does this mean that teachers will have less work?

Of course AIs can grade essays too, but does this mean that teachers will have less work? The jury is still out on this question: one major review of the research on AI in education found a conspicuous absence of reference to actual teachers. One scenario is a teacherless future where students are accelerated through courses of study by advanced robo-Profs.

A contrasting future has been foreseen by AI education expert Peirre Dillenbourg. He has predicted that we will have more teachers in the future, not less. He foresees teachers working in teams to oversee and design learning scenarios using multiple AIs dedicated to specific educational tasks.

That is the future taken care of but what about the present? Universities worldwide currently invest heavily in anti-plagiarism and academic integrity technologies. Many of these systems have been termed 'data-extractive', in that they often rely on extracting and mining large bodies of student work. At their worst these expensive systems can create climates of fear., where students feel they are being policed by big brother or sister.

With all of the fuss about AI, it is worth remembering that people are always at the heart of education

AI essay writing may be seen as just another chapter in the long history of so-called "essay mills", services that students can use to commission and buy their homework from. Will AI make these services redundant in the future? What constitutes cheating and breaches of academic integrity in the world of AI? After all, irrespective of how we define cheating, who loses if the student does not fully engage in their own learning?

Something that educators can do is to have conversations with students about their learning and especially their assessment. A guiding principle should be that a student will always want to do the work themselves given the right conditions. This is the opposite of a starting principle that says: every student is a potential cheater.

Assessment mixes that are not completely dependent on traditional essays can allow students to express themselves in a variety of ways. Do we try to tame AI to protect old ways of learning or should we embrace its potential and reimagine our assessment practices to reflect the modern reality of living in the 21st century? One creative educator had his students purposefully use and evaluate AI essay writers as part of their assignment.

With all of the fuss about AI, it is worth remembering that people are always at the heart of education. Student and teacher workloads should be key considerations in the design of assessment. Giving each other space to build trusting environments in which to teach and learn will require much human ingenuity, care and intelligence.

Dr Eamon Costello is an Associate Professor of Digital Learning at the DCU Institute of Education. Professor Mark Brown is Chair of Digital Learning and Director of the National Institute for Digital Learning at DCU.

The views expressed here are those of the author and do not represent or reflect the views of RT

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Will artificial intelligence be able to write my college essay? - RTE.ie