The year 1 and 2 modules are similar across all our Electronic Engineering courses and provide a grounding in essential engineering topics.

In years 3 and 4 youll specialise in AI, and can follow your interests by choosing modules from a wide range of options. You can also take modules from other subject areas.

Youll work in high-spec electronics and computer labs, equipped with the latest technology, hardware and software.

In the first year, youll study digital systems, and electrical materials and fields. There are core modules in:

mathematics

physics

electronics

programming

We'll develop your practical skills with extensive laboratory classes. In your first semester youll get to build processing boards.

Compulsory modules will explore:

electrical materials

circuitry

programming

electronic design

You'll choose from optional modules, covering topics such as:

photonics

semiconductors

computer engineering

At the end of the year, you'll complete a 3-week team challenge, judged by an industry panel. Previous projects include the development of a home AI system and building a quadcopter.

Youll complete a unique piece of individual research in an AI topic of your choice. This will typically involve designing, building and testing a new electronic system. Past students have designed a traffic counting system using computer vision, and explored security for smart home systems.

You'll study the foundations of machine learning, and select specialised optional modules such as:

robotic systems

computational biology

cyber security

green electronics

You can also choose to:

The main group design project is a great opportunity to experience working for an industry or academic customer. Past projects have involved:

Youll also select from optional modules covering topics, such as:

machine Learning

data mining

computer vision

You can apply to spend the second semester studying abroad at a partner institution.

Want more detail?See all the modules in the course.

Go here to read the rest:
Artificial Intelligence Engineering | University of Southampton

(ArtemisDiana/Shutterstock)

Artificial intelligence (AI) and machine learning (ML) are already changing the world but the innovations were seeing so far are just a taste of whats around the corner. We are on the precipice of a revolution that will affect every industry, from business and education to healthcare and entertainment. These new technologies will help solve some of the most challenging problems of our age and bring changes comparable in scale to the renaissance, the Industrial Revolution, and the electronic age.

While the printing press, fossil fuels, and silicon drove these past epochal shifts, a new generation of algorithms that automate tasks previously thought impossible will drive the next revolution. These new technologies will allow self-driving cars to identify traffic patterns, automate energy balancing in smart power grids, enable real-time language translation, and pioneer complex analytical tools that detect cancer before any human could ever perceive it.

Well, thats the promise of the AI and ML revolution, anyway. And to be clear, these things are all within our theoretical reach. But what the tech optimists tend to leave out is that our path to the bright, shiny AI future has some major potholes in it. One problem is looming especially large. We call it the dirty secret of AI and ML: right now, AI and ML dont scale well.

Scale the ability to expand a single machines capability to broader, more widespread applications is the holy grail of every digital business. And right now, AI and ML dont have it. While algorithms may hold the keys to our future, when it comes to creating them, were currently stuck in a painstaking, brute force methodology.

(paitoon/Shutterstock)

CreatingAI and ML algorithms isnt the hard part anymore. You tell them what to learn, feed them the right data, and they learn how to parse novel data without your help. The labor-intensive piece comes when you want the algorithms to operate in the real world. Left to their own devices, AI will suck up as much time, compute, and data/bandwidth as you give it. To be truly effective, these algorithms need to run lean, especially now that businesses and consumers are showing an increasing appetite for low-latency operations at the edge. Getting your AI to run in an environment where speed, compute,

and bandwidth are all constrained is the real magic trick here.

Thus, optimizing AI and ML algorithms has become the signature skill of todays AI researchers/engineers. Its expensive in terms of time, resources, money, and talent, but essential if you want performantAI. However, today, the primary way were addressing the problem is via brute force throwing bodies at the problem. Unfortunately, the demand for these algorithms is exploding while the pool of qualified AI engineers remains relatively static. Even if it were economically feasible to hire them, there are not enough trained AI engineers to work on all the projects that will take the world to the resplendent AI/sci-fi future weve been promised.

But all is not lost. There is a way for us to get across the threshold to achieve the exponential AI advances we require. The answer to scaling AI and ML algorithms is actually a simple idea. Train ML algorithms to tune ML algorithms, an approach the industry calls Automated Machine Learning, or AutoML. Tuning AI and ML algorithms may be more of an art than a science, but then again, so is driving, photo retouching, and instant language translation, all of which are addressable via AI and ML.

(Phonlamai Photo/Shutterstock)

AutoML will allow us to scale AI optimization so it can achieve full adoption throughout computing, including at the edge where latency and compute are constrained. By using hardware awareness in AutoML, we can push performance even further. We believe this approach will also lead to a world where the barrier to entry for AI programmers is lower, allowing more people to enter the field, and making better use of high-level programmers. Its our hope that the resulting shift will alleviate the current talent bottleneck the industry is facing.

Over the next few years, we expect to automate various AI optimization techniques such as pruning, distillation, neural architecture search, and others, to achieve 15-30x performance improvements. Googles EfficientNet research has also yielded very promising results in the field of auto-scaling convolutional neural networks. Another example is DataRobots AutoML tools, which can be applied to automating the tedious and time-consuming manual work required for data preparation and model selection.

There is one last hurdle to cross, though. AI automates tasks we always assumed we needed humans to do, offloading these difficult feats to a computer programmed by a clever AI engineer. The dream of AutoML is to offload the work another level, using AI algorithms to tune and create new AI algorithms. But theres no such thing as a free lunch. We will now need evenmore highlyskilled programmers to develop the AutoML routines at the meta-level. The good news is, we think weve got enough of them to do this.

But its not all about growing the field from the top. This innovation not only expands the pool of potential programmers, allowing lower-level programmers to create highly effective AI it provides a de facto training path to move them into higher and higher-skilled positions. This in turn will create a robust talent pipeline that can supply the industry for years to come and ensure we have a good supply of hardcore AI developers for when we hit the next bottleneck. Because yes, there may come a day when we need Auto-AutoML, but for now, we want to take things one paradigm-shifting innovation at a time. It may sound glib, but we believe it wholeheartedly: the answer to the problems of AI is more AI.

About the authors: Nilesh Jain is a Principal Engineer at Intel Labs where he leads Emerging Visual/AI Systems Research Lab. He focuses on developing innovative technologies for edge/cloud systems for emerging workloads. His current research interests include visual computing, hardware aware AutoML systems. He received M.Sc. degree from Oregon Graduate Institute/OHSU. He is also Sr. IEEE member, and has published over 15 papers and over 20 patents.

Ravi Iyer is an Intel Fellow in Intel Labs where he leads the Emerging Systems Lab. His research interests include developing innovative technologies, architectures and edge/cloud systems for emerging workloads. He has published over 150 papers and has over 40 patents granted. He received his Ph.D. in Computer Science from Texas A&M. He is also an IEEE Fellow.

Related Items:

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Artificial Intelligence and Machine Learning Are Headed for A Major Bottleneck Here's How We Solve It - Datanami

Dublin, June 08, 2022 (GLOBE NEWSWIRE) -- The "Global Artificial Intelligence (AI) Partnering Terms and Agreements 2010 to 2022" report has been added to ResearchAndMarkets.com's offering.

This report contains a comprehensive listing of all artificial intelligence partnering deals announced since 2010 including financial terms where available including over 750 links to online deal records of actual artificial intelligence partnering deals as disclosed by the deal parties.

The report provides a detailed understanding and analysis of how and why companies enter artificial intelligence partnering deals. The majority of deals are early development stage whereby the licensee obtains a right or an option right to license the licensors artificial intelligence technology or product candidates. These deals tend to be multicomponent, starting with collaborative R&D, and commercialization of outcomes.

This report provides details of the latest artificial intelligence, oligonucletides including aptamers agreements announced in the healthcare sectors.

Understanding the flexibility of a prospective partner's negotiated deals terms provides critical insight into the negotiation process in terms of what you can expect to achieve during the negotiation of terms. Whilst many smaller companies will be seeking details of the payments clauses, the devil is in the detail in terms of how payments are triggered - contract documents provide this insight where press releases and databases do not.

In addition, where available, records include contract documents as submitted to the Securities Exchange Commission by companies and their partners.

Contract documents provide the answers to numerous questions about a prospective partner's flexibility on a wide range of important issues, many of which will have a significant impact on each party's ability to derive value from the deal.

In addition, a comprehensive appendix is provided organized by artificial intelligence partnering company A-Z, deal type definitions and artificial intelligence partnering agreements example. Each deal title links via Weblink to an online version of the deal record and where available, the contract document, providing easy access to each contract document on demand.

The report also includes numerous tables and figures that illustrate the trends and activities in artificial intelligence partnering and dealmaking since 2010.

In conclusion, this report provides everything a prospective dealmaker needs to know about partnering in the research, development and commercialization of artificial intelligence technologies and products.

Report scope

Global Artificial Intelligence Partnering Terms and Agreements includes:

In Global Artificial Intelligence Partnering Terms and Agreements, the available contracts are listed by:

Key Topics Covered:

Executive Summary

Chapter 1 - Introduction

Chapter 2 - Trends in artificial intelligence dealmaking2.1. Introduction2.2. Artificial intelligence partnering over the years2.3. Most active artificial intelligence dealmakers2.4. Artificial intelligence partnering by deal type2.5. Artificial intelligence partnering by therapy area2.6. Deal terms for artificial intelligence partnering2.6.1 Artificial intelligence partnering headline values2.6.2 Artificial intelligence deal upfront payments2.6.3 Artificial intelligence deal milestone payments2.6.4 Artificial intelligence royalty rates

Chapter 3 - Leading artificial intelligence deals3.1. Introduction3.2. Top artificial intelligence deals by value

Chapter 4 - Most active artificial intelligence dealmakers4.1. Introduction4.2. Most active artificial intelligence dealmakers4.3. Most active artificial intelligence partnering company profiles

Chapter 5 - Artificial intelligence contracts dealmaking directory5.1. Introduction5.2. Artificial intelligence contracts dealmaking directory

Chapter 6 - Artificial intelligence dealmaking by technology type

AppendicesAppendix 1 - Artificial intelligence deals by company A-ZAppendix 2 - Artificial intelligence deals by stage of developmentAppendix 3 - Artificial intelligence deals by deal typeAppendix 4 - Artificial intelligence deals by therapy areaAppendix 5 - Deal type definitionsAppendix 6 - Further reading on dealmaking

Table of figuresFigure 1: Artificial intelligence partnering since 2010Figure 2: Active artificial intelligence dealmaking activity since 2010Figure 3: Artificial intelligence partnering by deal type since 2010Figure 4: Artificial intelligence partnering by disease type since 2010Figure 5: Artificial intelligence deals with a headline valueFigure 6: Artificial intelligence deals with an upfront valueFigure 7: Artificial intelligence deals with a milestone valueFigure 8: Artificial intelligence deals with a royalty rate valueFigure 9: Top artificial intelligence deals by value since 2010Figure 10: Most active artificial intelligence dealmakers since 2010

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/irt217

About ResearchAndMarkets.comResearchAndMarkets.com is the world's leading source for international market research reports and market data. We provide you with the latest data on international and regional markets, key industries, the top companies, new products and the latest trends.

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Global Artificial Intelligence (AI) Partnering Deal Terms and Agreements Report 2022: Latest AI, Oligonucletides Including Aptamers Agreements...

Netradyne Uses AI To Help Fleets Reduce Driver Incidents, Protect Against False Claims, and Create Safer Roads

SAN DIEGO, June 8, 2022 /PRNewswire/ --Netradyne, an industry leader in artificial intelligence (AI) and edge computing focusing on driver and fleet safety, has been named on this year's Forbes AI 50 list 2022 for North America. Produced in partnership with Sequoia Capital, this list recognizes the standout privately held companies in North America that are making the most interesting and impactful uses of AI.

Forbes editorial team acknowledged that AI technology is driving advancements in every industry but that it can be difficult to identify which companies are utilizing such technology in transformative and measurable ways. The Forbes AI 50 list, now in its fourth edition, identifies North America's privately held companies at the forefront of the field for whom AI is at the heart of their products and services.

In selecting honorees for this year's list, Forbes' 12-judge panel of experts in artificial intelligence from the fields of academia, technology, and venture capital evaluated hundreds of submissions, handpicking the top 50 most compelling companies.

"We are honored to be named to the Forbes AI 50 list," said Avneesh Agrawal, co-founder, and CEO of Netradyne. "At Netradyne, our mission is to create safer and smarter roadways for all. Using AI and edge computing technologies, we are revolutionizing the fleet transportation ecosystem by helping reinforce good driving behavior and similarly empowering drivers to improve their performance."

Agrawal continued, "Driveri's unique ability to analyze everymile of a journey allows insights into good driving behaviors, which can be recognized and rewarded to reinforce drivers' safe behavior, and drivers also have full transparency and coaching access to their personalized driving GreenZone score via the driver mobile app."

Netradyne provides fleets of all sizes and vehicle types with an advanced video safety camera, fleet performance analytics tracking, and driver awareness tools to help reduce risky driving behavior and reward safe driving decision-making. Driveriis the only solution that can positively recognize, empower and improve driver performance. The cascading effects are powerful by using Driveri's revolutionary AI and reinforcing good behavior to improve driver performance in real-time. Fleets see reduced accidents, higher safety scores, lower insurance costs, improved driver retention, and better fleet performance in increased profits.

Netradyne was one of the hundreds of applicants to be included in this prestigious list. A panel of 12 expert AI judges identified the 50 most compelling companies.

About Netradyne, Inc.

Netradyne harnesses the power of Computer Vision and Edge Computing to revolutionize the modern-day transportation ecosystem. Netradyne is an industry leader in fleet safety solutions, immediately improving driver behavior and fleet performance and setting commercial vehicle driving standards. Netradyne collects and analyzes more data points and meaningful information than any other fleet safety organization so customers can improve retention, increase profitability, enhance safety, and enable end-to-end transparency. Organizations trust Netradyne to build a positive, safe, and driver-focused culture to take their business to the next level.

CONTACT: [emailprotected]

SOURCE Netradyne

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Netradyne Named to Forbes AI 50 List of Top Artificial Intelligence Companies of 2022 - PR Newswire

The way Timnit Gebru sees it, the foundations of the future are being built now. In Silicon Valley, home to the worlds biggest tech companies, the artificial intelligence (AI) revolution is already well under way. Software is being written and algorithms are being trained that will determine the shape of our lives for decades or even centuries to come. If the tech billionaires get their way, the world will run on artificial intelligence.

Cars will drive themselves and computers will diagnose and cure diseases. Art, music and movies will be automatically generated. Judges will be replaced by software that supposedly applies the law without bias and industrial production lines will be fully automated and exponentially more efficient.

Decisions on who gets home loans or how much your insurance premiums will be made by an algorithm that assesses your creditworthiness, while a similar algorithm will sift through job applications before any CVs get to a human recruiter (this is already happening in many industries). Even news stories, like this one, will be written by a program that can do it faster and more accurately than human journalists.

But what if those algorithms are racist, exclusionary or have dangerous implications that were not anticipated by the mostly rich, white men who created them? What if, instead of making the world better, they just reinforce the inequalities and injustices of the present? Thats what Gebru is worried about.

Were really seeing it happening. Its scary. Its reinforcing so many things that are harming Africa, says Gebru.

She would know. Gebru was, until late 2020, the co-director of Googles Ethical AI program. Like all the big tech companies, Google is putting enormous resources into developing its artificial intelligence capabilities and figuring out how to apply them in the real world. This encompasses everything from self-driving cars to automatic translation and facial recognition programs.

The ultimate prize is a concept known as Artificial General Intelligence a computer that is capable of understanding the world as well as any human and making decisions accordingly

It sounds like a god, says Gebru.

She was not at Google for long. Gebru joined in 2018, and it was her job to examine how all this new technology could go wrong. But input from the ethics department was rarely welcomed.

It was just screaming about issues and getting retaliated against, she says. The final straw was when she co-authored a paper on the ethical dangers of large language models, used for machine translation and autocomplete, which her bosses told her to retract.

In December 2020, Gebru left the company. She says she was fired; Google says she resigned. Either way, her abrupt departure and the circumstances behind it thrust her into the limelight, making her the most prominent voice in the small but growing movement that is trying to force a reckoning with Big Tech before it is too late to prevent the injustices of the present being replicated in the future.

Gebru is one of the worlds leading researchers helping us understand the limits of artificial intelligence in products like facial-recognition software, which fails to recognise women of colour, especially black women, wrote Time magazine when it nominated Gebru as one of the 100 most influential people in the world in 2022.

She offers us hope for justice-oriented technology design, which we need now more than ever.

Artificial intelligence is not yet as intelligent as it sounds. We are not at the stage where a computer can think for itself or match a human brain in cognitive ability. But what computers can do is process incomprehensibly vast amounts of data and then use that data to respond to a query. Take Dall-E 2, the image-generation software that created The Continents cover illustration this week, developed by San Francisco-based OpenAI.

It can take a prompt such as a brain riding a rocket ship heading towards the moon and turn it into an image with uncannily accurate sometimes eerie results. But the software is not thinking for itself. It has been trained on data, in this case, 650 million existing images, each of which have a text caption telling the computer what is going on in the picture. This means it can recognise objects and artistic styles and regurgitate them on command. Without this data, there is no artificial intelligence.

Like coal shovelled into a steamships furnace, data is the raw material that fuels the AI machine. Gebru argues that all too often the fuel is dirty. Perhaps the data is scraped from the internet, which means it is flawed in all the ways the internet itself is flawed Anglo- and Western-centric, prone to extremes of opinion and political polarisation and all too often it reinforces stereotypes and prejudices. Dall-E 2, for instance, thinks that a CEO must be a white man, while nurses and flight attendants are all women.

More ominous still was an algorithm developed for the United States prison system, which predicted that black prisoners were more likely than white people to commit another crime, which led to black people spending longer in jail.

Or perhaps, in one of the great paradoxes of the field, the data is mined through old-fashioned manual labour thousands of people hunched over computer screens, painstakingly sorting and labelling images and videos. Most of this work has been outsourced to the developing world and the people doing the work certainly arent receiving Silicon Valley salaries.

Where do you think this huge workforce is? There are people in refugee camps in Kenya, in Venezuela, in Colombia, that dont have any sort of agency, says Gebru.

These workers are generating the raw material but the final product and the enormous profits that are likely to come with it will be made for and in the West. What does this sound like to you? Gebru asks.

Timnit Gebru grew up in Addis Ababa (Timnit means wish in Tigrinya). She was 15 when Ethiopia went to war with Eritrea, forcing her into exile, first in Ireland and then in the US, where she first experienced casual racism. A temp agency boss told her mother to get a job as a security guard, because who knows whatever degree you got from Africa. A teacher refused to place Gebru in an advanced class because people like you always fail.

But Gebru didnt fail. Her academic record got her into Stanford, one of the worlds most prestigious universities, where she hung out with her friends in the African Students Association and studied electrical engineering. It was here that both her technical ability and her political consciousness grew.

She worked at Apple for a stint, and then returned to the university where she developed a growing fascination with artificial intelligence. So then I started going to these conferences in AI or machine learning, and I noticed that there were almost no black people. These conferences would have 5 000 or 6 000 people from all over the world but one or two black people.

Gebru co-founded Black in AI for black professionals in the industry to come together and figure out ways to increase representation. By that stage, her research had already proved how this racial inequality was being replicated in the digital world. A landmark paper she co-authored with the Ghanaian-American-Canadian computer scientist, Joy Buolamwini, found that facial recognition software is less accurate at identifying women and people of colour a big problem if law enforcement is using this software to identify suspects.

Gebru got her job at Google a couple of years later. It was a chance to fix what was broken from inside one of the biggest tech companies in the world. But, according to Gebru, the company did not want to hear about the environmental costs of processing vast data sets, or the baked in biases that come with them, or the exploitation of workers in the Global South. It was too busy focusing on all the good it was going to do in the distant future to worry about the harm it might cause in the present.

This, she says, is part of a pernicious philosophy known as long-termism, which holds that lives in the future are worth just as much as lives in the present. Its taken a really big hold in Silicon Valley, Gebru says. This philosophy is used by tech companies and engineers to justify decisions in product design and software development that do not prioritise immediate crises such as poverty, racism and climate change or take other parts of the world into consideration.

Abeba Birhane, a senior fellow in Trustworthy AI at the Mozilla Foundation, says: The way things are happening right now is predicated on the exploitation of people on the African continent. That model has to change. Not only is long-termism taking up so much of the AI narrative, it is something that is preoccupied with first-world problems.

Its taking up a lot of air, attention, funding, from the kind of work Timnit is doing, the groundwork that specialist scholars of colour are doing on auditing data sets, auditing algorithms, exposing biases and toxic data sets.

In the wake of Gebrus departure from Google some 2 000 employees signed a petition protesting against her dismissal. Although not acknowledging any culpability, Sundar Pichai the chief executive of Alphabet, Googles parent company said: We need to assess the circumstances that led to Dr Gebrus departure, examining where we could have improved and led a more respectful process. We will begin a review of what happened to identify all the points where we can learn.

In November 2020, a civil war broke out in Ethiopia and once again Gebrus personal and professional worlds collided. As an Ethiopian, she has been vocal in raising the alarm about atrocities being committed, including running a fundraiser for victims of the conflict. As a computer scientist, she has watched in despair as artificial intelligence has enabled and exacerbated these atrocities.

On Facebook, hate speech and incitements to violence related to the Ethiopian conflict have spread with deadly consequences, with the companys algorithms and content moderators entirely unable or unwilling to stop it. For example, an investigation by The Continent last year, based on a trove of leaked Facebook documents, showed how the social media giants integrity team flagged a network of problematic accounts calling for a massacre in a specific village. But no action was taken against the accounts. Shortly afterwards, a massacre took place.

The tide of the war was turned when the Ethiopian government procured combat drones powered by artificial intelligence. The drones targeted the rebel Tigray forces with devastating efficacy and have been implicated in targeting civilians too, including in the small town of Dedebit, where 59 people were killed when a drone attacked a camp for internally displaced people.

Thats why all of us need to be concerned about AI, says Gebru. It is used to consolidate power for the powerful. A lot of people talk about AI for the social good. But to me, when you think of the current way it is developed, it is always used for warfare. Its being used in a lot of different ways by law enforcement, by governments to spy on their citizens,by governments to be at war with their citizens, and by corporations to maximise profit.

Once again, Gebru is doing something about it. Earlier this year, she launched the Distributed Artificial Intelligence Research Institute (Dair). The clue that Dair operates a little differently is in the word distributed.

Instead of setting up in Silicon Valley, Dairs staff and fellows will be distributed all around the world, rooted in the places they are researching.

How do we ring the alarm about the bad things that we see, and how can we develop this research in a way that benefits our community? Raesetje Sefala, Dairs Johannesburg-based research fellow, puts it like this: At the moment, it is people in the Global North making decisions that will affect the Global South.

As she explains it, Dairs mission is to convince Silicon Valley to take its ethical responsibilities more seriously but also to persuade leaders in the Global South to make better decisions and to implement proper regulatory frameworks. For instance, Gmail passively scans all emails in Africa for the purposes of targeted advertising, but the European Union has outlawed this to protect their citizens.

Our governments need to ask better questions, says Sefala. If it is about AI for Johannesburg, they should be talking to the researchers here.

So far, Dairs team is small just seven people in four countries. So, too, is the budget.

What were up against is so huge, the resources, the money that is being spent, the unity with which they just charge ahead. Its daunting sometimes if you think about it too much, so I try not to, says Gebru.

And yet, as Gebrus Time magazine nod underscored, sometimes it is less about the money and more about the strength of the argument. On that score, Gebru and Dair are well ahead of Big Tech and their not quite all-powerful algorithms.

This article first appeared in The Continent, the pan-African weekly newspaper produced in partnership with the Mail & Guardian. Its designed to be read and shared on WhatsApp. Download your free copy here.

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Timnit Gebru and the fight to make artificial intelligence work for Africa - Mail and Guardian