This accredited course is designed to give you industry experience alongside our research-led teaching.

We encourage you to take summer work placements in an industry of your choice or even add a full year in industry to help you gain the experience you need for accreditation.

All our computer science degree courses share the same compulsory modules in years 1 and 2, making it easy to switch between them. In the third and fourth years, you can tailor your degree by choosing optional modules.

Youll study the logical and mathematical theory underpinning computer science. Youll also gain an understanding of the fundamentals of computer hardware.

As an introduction to software engineering, youll cover data structures and algorithms. Youll also look at the principles of AI programming, including using an object-oriented approach and software engineering processes.

Youll apply your knowledge by working on practical projects. For example, youll build algorithms and data analysis tools, and develop software user interfaces.

Youll deepen your understanding of computer science by studying topics, such as artificial intelligence, communication protocols and the TCP/IP layered model.

A group project will give you first-hand experience of working in a team, and of the problems of communication and scale in software engineering.

An individual project is a chance to explore in depth an area of AI that interests you, under the supervision of an academic who is doing work in that area. Recent topics include:

Youll take a compulsory module in engineering management and law. Youll also specialise in artificial intelligence choosing options such as machine learning, simulation and advanced robotics.

You could also study a language, take modules from other disciplines such as psychology or chemistry, or choose from a range of innovative interdisciplinary modules.

Youll take part in a group design project. This involves working in a team for an industry or academic customer to solve a real-world problem. For example, previous students built an AI system for Ordnance Survey for a project entitled learning from aerial imagery.

Optional modules cover topics such as machine learning, computational finance and biologically inspired robots.

There is also an opportunity to study abroad for a semester.

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

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Computer Science & Artificial Intelligence - University of Southampton

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.

Originally posted here:
Artificial Intelligence Engineering | University of Southampton

To the average person, it must seem as if the field of artificial intelligence is making immense progress. According to the press releases, and some of the more gushing media accounts, OpenAIs DALL-E 2 can seemingly create spectacular images from any text; another OpenAI system called GPT-3 can talk about just about anything; and a system called Gato that was released in May by DeepMind, a division of Alphabet, seemingly worked well on every taskthe company could throw at it. One of DeepMinds high-level executives even went so far as to brag that in the quest for artificial general intelligence (AGI), AI that has the flexibility and resourcefulness of human intelligence, The Game is Over! And Elon Musk said recently that he would be surprised if we didnt have artificial general intelligence by 2029.

Dont be fooled. Machines may someday be as smart as people, and perhaps even smarter, but the game is far from over. There is still an immense amount of work to be done in making machines that truly can comprehend and reason about the world around them. What we really need right now is less posturing and more basic research.

To be sure, there are indeed some ways in which AI truly is making progresssynthetic images look more and more realistic, and speech recognition can often work in noisy environmentsbut we are still light-years away from general purpose, human-level AI that can understand the true meanings of articles and videos, or deal with unexpected obstacles and interruptions. We are still stuck on precisely the same challenges that academic scientists (including myself) having been pointing out for years: getting AI to be reliable and getting it to cope with unusual circumstances.

Take the recently celebrated Gato, an alleged jack of all trades, and how it captioned an image of a pitcher hurling a baseball. The system returned three different answers: A baseball player pitching a ball on top of a baseball field, A man throwing a baseball at a pitcher on a baseball field and A baseball player at bat and a catcher in the dirt during a baseball game. The first response is correct, but the other two answers include hallucinations of other players that arent seen in the image. The system has no idea what is actually in the picture as opposed to what is typical of roughly similar images. Any baseball fan would recognize that this was the pitcher who has just thrown the ball, and not the other way aroundand although we expect that a catcher and a batter are nearby, they obviously do not appear in the image.

A baseball player pitching a ballon top of a baseball field.A man throwing a baseball at apitcher on a baseball field.A baseball player at bat and acatcher in the dirt during abaseball game

Likewise, DALL-E 2 couldnt tell the difference between a red cube on top of a blue cube and a blue cube on top of a red cube. A newer version of the system, released in May, couldnt tell the difference between an astronaut riding a horse and a horse riding an astronaut.

When systems like DALL-E make mistakes, the result is amusing, but other AI errors create serious problems. To take another example, a Tesla on autopilot recently drove directly towards a human worker carrying a stop sign in the middle of the road, only slowing down when the human driver intervened. The system could recognize humans on their own (as they appeared in the training data) and stop signs in their usual locations (again as they appeared in the trained images), but failed to slow down when confronted by the unusual combination of the two, which put the stop sign in a new and unusual position.

Unfortunately, the fact that these systems still fail to be reliable and struggle with novel circumstances is usually buried in the fine print. Gato worked well on all the tasks DeepMind reported, but rarely as well as other contemporary systems. GPT-3 often creates fluent prose but still struggles with basic arithmetic, and it has so little grip on reality it is prone to creating sentences like Some experts believe that the act of eating a sock helps the brain to come out of its altered state as a result of meditation, when no expert ever said any such thing. A cursory look at recent headlines wouldnt tell you about any of these problems.

The subplot here is that the biggest teams of researchers in AI are no longer to be found in the academy, where peer review used to be coin of the realm, but in corporations. And corporations, unlike universities, have no incentive to play fair. Rather than submitting their splashy new papers to academic scrutiny, they have taken to publication by press release, seducing journalists and sidestepping the peer review process. We know only what the companies want us to know.

In the software industry, theres a word for this kind of strategy: demoware, software designed to look good for a demo, but not necessarily good enough for the real world. Often, demoware becomes vaporware, announced for shock and awe in order to discourage competitors, but never released at all.

Chickens do tend to come home to roost though, eventually. Cold fusion may have sounded great, but you still cant get it at the mall. The cost in AI is likely to be a winter of deflated expectations. Too many products, like driverless cars, automated radiologists and all-purpose digital agents, have been demoed, publicizedand never delivered. For now, the investment dollars keep coming in on promise (who wouldnt like a self-driving car?), but if the core problems of reliability and coping with outliers are not resolved, investment will dry up. We will be left with powerful deepfakes, enormous networks that emit immense amounts of carbon, and solid advances in machine translation, speech recognition and object recognition, but too little else to show for all the premature hype.

Deep learning has advanced the ability of machines to recognize patterns in data, but it has three major flaws. The patterns that it learns are, ironically, superficial, not conceptual; the results it creates are difficult to interpret; and the results are difficult to use in the context of other processes, such as memory and reasoning. As Harvard computer scientist Les Valiant noted, The central challenge [going forward] is to unify the formulation of learning and reasoning. You cant deal with a person carrying a stop sign if you dont really understand what a stop sign even is.

For now, we are trapped in a local minimum in which companies pursue benchmarks, rather than foundational ideas, eking out small improvements with the technologies they already have rather than pausing to ask more fundamental questions. Instead of pursuing flashy straight-to-the-media demos, we need more people asking basic questions about how to build systems that can learn and reason at the same time. Instead, current engineering practice is far ahead of scientific skills, working harder to use tools that arent fully understood than to develop new tools and a clearer theoretical ground. This is why basic research remains crucial.

That a large part of the AI research community (like those that shout Game Over) doesnt even see that is, well, heartbreaking.

Imagine if some extraterrestrial studied all human interaction only by looking down at shadows on the ground, noticing, to its credit, that some shadows are bigger than others, and that all shadows disappear at night, and maybe even noticing that the shadows regularly grew and shrank at certain periodic intervalswithout ever looking up to see the sun or recognizing the three-dimensional world above.

Its time for artificial intelligence researchers to look up. We cant solve AI with PR alone.

This is an opinion and analysis article, and the views expressed by the author or authors are not necessarily those of Scientific American.

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Artificial General Intelligence Is Not as Imminent as You Might Think - Scientific American

Redding, California, June 09, 2022 (GLOBE NEWSWIRE) -- According to a new market research report titled, AI in Cybersecurity Market by Technology (ML, NLP), Security (Endpoint, Cloud, Network), Application (DLP, UTM, IAM, Antivirus, IDP), Industry (Retail, Government, BFSI, IT, Healthcare), and Geography - Global Forecasts to 2029, the global artificial intelligence in cybersecurity market is expected to grow at a CAGR of 24.2% during the forecast period to reach $66.22 billion by 2029.

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The increasing demand for advanced cybersecurity solutions and privacy, the growing significance of AI-based cybersecurity solutions in the banking sector, the rising frequency and complexity of cyber threats are the key factors driving the growth of the artificial intelligence in cybersecurity market. In addition, the growing need for AI-based cybersecurity solutions among small and medium-sized enterprises (SMEs) are creating new growth opportunities for vendors in the AI in cybersecurity market.

However, the lack of skilled AI professionals, the perception of AI in cybersecurity as an uncomprehensive security solution, and the impacts of the COVID-19 pandemic are expected to restrain the growth of this market to a notable extent.

The global artificial intelligence in cybersecurity market is segmented based on components (hardware, software, services), technology (machine learning, natural language processing, context-aware computing), security (application security, endpoint security, cloud security, network security), by applications (data loss prevention, unified threat management, encryption, identity & access management, risk & compliance management, antivirus/antimalware, intrusion detection/prevention system, distributed denial of service mitigation, security information & event management, threat intelligence, fraud detection), by deployment (on-premises, cloud-based), industry vertical (retail, government & defense, automotive & transportation, BFSI, manufacturing, infrastructure, IT & telecommunication, healthcare, aerospace, education, energy). The study also evaluates industry competitors and analyses the market at the country level.

Based on component, the AI in cybersecurity market is segmented into software, hardware, and services. In 2022, the software segment is estimated to account for the largest share of the artificial intelligence in cybersecurity market. The larger share and highest CAGR of this segment is primarily driven by the growing data security concerns, the increase in demand for AI platforms solutions for security operations, the surge in demand for robust and cost-effective security solutions among business enterprises to strengthen their cybersecurity infrastructure.

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Based on technology, the market is segmented into machine learning, natural language processing (NLP), and context-aware computing. In 2022, the machine learning technology segment is estimated to account for the largest share of the artificial intelligence in cybersecurity market. The large share and highest CAGR of this segment is primarily attributed to its advanced ability to collect, process, and handle big data from different sources that offer rapid analysis and prediction. It also helps analyze user behavior and learn from them to help prevent attacks and respond to changing behavior. In addition, it helps find threats and respond to active attacks in real-time, reduces the amount of time spent on routine tasks, and enables organizations to use their resources more strategically, further supporting the growth of the machine learning technology market in the coming years.

Based on security, the market is segmented into network security, cloud security, endpoint security, and application security. In 2022, the network security segment is estimated to account for the largest share of the artificial intelligence in cybersecurity market. The large share of this segment is attributed to the adoption of the Bring Your Own Device (BYOD) trend, the increasing number of APTs, malware, and phishing attacks, the increasing need for secure data transmission, the growing demand for network security solutions, and rising privacy concerns. However, the cloud security segment is slated to register the highest CAGR during the forecast period due to the increased adoption of Internet of Things (IoT) devices, surge in the deployment of cloud solutions, the emergence of remote work and collaboration, the increasing demand for robust and cost-effective security services.

Based on application, this market is segmented into data loss prevention, unified threat management, encryption, identity & access management, risk & compliance management, intrusion detection/prevention system, antivirus/antimalware, distributed denial of service (DDoS) mitigation, Security Information and event management (SIEM), threat intelligence, and fraud detection. In 2022, the identity and access management segment is estimated to account for the largest share of the artificial intelligence in cybersecurity market. The large share of this segment is attributed to the increase in security concerns among organizations, the increasing number and complexity of cyber-attacks, the growing need for integrity & safety of confidential information in industry verticals, and the growing emphasis on compliance management. However, the data loss prevention segment is slated to register the highest CAGR during the forecast period due to the increasing regulatory and compliance requirements and the growing need to address data-related threats, including the risks of accidental data loss and exposure of sensitive data in organizations.

Quick Buy Artificial Intelligence in Cybersecurity Market by Technology (ML, NLP), Security (Endpoint, Cloud, Network), Application (DLP, UTM, IAM, Antivirus, IDP), Industry (Retail, Government, BFSI, IT, Healthcare), and Region - Global Forecasts to 2029 Research Report: https://www.meticulousresearch.com/Checkout/30331808

Based on industry vertical, the market is segmented into government & defense, retail, manufacturing, banking, financial services, and insurance (BFSI), automotive & transportation, healthcare, IT & telecommunication, aerospace, education, and energy. In 2022, the IT & telecommunication sector is estimated to account for the largest share of the AI in cybersecurity market. The large share of this segment is mainly attributed to increasing incidence of security breaches by cybercriminal, shifting preference from traditional business models to sophisticated technologies, and including IoT devices, 5G, and cloud computing. However, the healthcare sector is slated to register the highest CAGR during the forecast period due to the rising sophistication levels of cyber-attacks, the growing incorporation of advanced cybersecurity solutions, the exponential rise in healthcare data breaches, and the growing adoption of IoT & connected devices across the healthcare sector.

Based on deployment, the market is segmented into on-premises and cloud-based. In 2022, the on-premises segment is estimated to account for the largest share of the artificial intelligence in cybersecurity market. The large share of this segment is attributed to the increasing necessity for enhancing the internal processes & systems, security issues related to cloud-based deployments, and the rising demand for advanced security application software among industry verticals. However, the cloud-based segment is slated to register the highest CAGR during the forecast period due to the increasing number of large enterprises using cloud platforms for data repositories and the growing demand to reduce the capital investment required to implement cybersecurity solutions. In addition, several organizations are moving operations to the cloud, leading cybersecurity vendors to develop cloud-based solutions.

Based on geography, in 2022, North America is estimated to account for the largest share of the overall artificial intelligence in cybersecurity market. The large market share of North America is attributed to the presence of major players along with several emerging startups in the region, the increase in government initiatives towards advanced technologies, such as artificial intelligence, the proliferation of cloud-based solutions, the increasing sophistication in cyber-attacks, and the emergence of disruptive digital technologies. However, Asia-Pacific is expected to register the highest CAGR during the forecast period. Factors such as the rising number of connected devices, the increasing privacy & security concerns, the growing awareness regarding cybersecurity among organizations, rapid economic development, high adoption of advanced technologies, such as IoT, 5G technology, and cloud computing are contributing to the growth of this market in Asia-Pacific.

The report also includes an extensive assessment of the key strategic developments adopted by the leading market participants in the industry over the past four years (20192022). The artificial intelligence in cybersecurity market has witnessed several partnerships & agreements in recent years that enabled companies to broaden their product portfolios, advance the capabilities of existing products, and gain cost leadership in the cybersecurity market. For instance, in 2021, Juniper Networks, Inc. (U.S.) launched Juniper Cloud Workload Protection, a software designed to automatically defend application workloads in any cloud or on-premises data center environment against application exploits in real-time. Similarly, in 2021, SecurityBridge (Germany) partnered with Fortinet, Inc. (U.S.) to address the security challenges posed by vulnerabilities within the SAP landscape. Also, in 2021, Check Point Software Technologies Ltd. (Israel) launched security gateways to protect SMBs against threats.

The global artificial intelligence in cybersecurity market is fragmented in nature. The major players operating in this market are Amazon Web Services, Inc. (U.S.), IBM Corporation (U.S.), Intel Corporation (U.S.), Microsoft Corporation (U.S.), Nvidia Corporation (U.S.), FireEye, Inc. (U.S.), Palo Alto Networks, Inc. (U.S.), Juniper Networks, Inc. (U.S.), Fortinet, Inc. (U.S.), Cisco Systems, Inc. (U.S.), Micron Technology, Inc. (U.S.), Check Point Software Technologies Ltd. (U.S.), Imperva (U.S.), McAfee LLC (U.S.), LogRhythm, Inc. (U.S.), Sophos Ltd. (U.S.), NortonLifeLock Inc. (U.S.), and Crowdstrike Holdings, Inc. (U.S.).

To gain more insights into the market with a detailed table of content and figures, click here:https://www.meticulousresearch.com/product/artificial-intelligence-in-cybersecurity-market-5101

Scope of the Report:

AI in CybersecurityMarket by Component

AI in CybersecurityMarket by Technology

AI in CybersecurityMarket by Security Type

AI in CybersecurityMarket by Application

AI in Cybersecurity Market by Deployment Type

AI in CybersecurityMarket by Industry Vertical

AI in CybersecurityMarket by Geography:

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Meticulous Research was founded in 2010 and incorporated as Meticulous Market Research Pvt. Ltd. in 2013 as a private limited company under the Companies Act, 1956. Since its incorporation, the company has become the leading provider of premium market intelligence in North America, Europe, Asia-Pacific, Latin America, and the Middle East & Africa.

The name of our company defines our services, strengths, and values. Since the inception, we have only thrived to research, analyze, and present the critical market data with great attention to details. With the meticulous primary and secondary research techniques, we have built strong capabilities in data collection, interpretation, and analysis of data including qualitative and quantitative research with the finest team of analysts. We design our meticulously analyzed intelligent and value-driven syndicate market research reports, custom studies, quick turnaround research, and consulting solutions to address business challenges of sustainable growth.

Contact:Mr.Khushal BombeMeticulous Market Research Inc.1267WillisSt,Ste200 Redding,California,96001, U.S.USA: +1-646-781-8004Europe : +44-203-868-8738APAC: +91 744-7780008Email-sales@meticulousresearch.comVisit Our Website:https://www.meticulousresearch.com/Connect with us on LinkedIn-https://www.linkedin.com/company/meticulous-researchContent Source: https://www.meticulousresearch.com/pressrelease/56/artificial-intelligence-in-cybersecurity-market-2029

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Artificial Intelligence in Cybersecurity Market Worth $66.22 Billion by 2029 - Exclusive Report by Meticulous Research - GlobeNewswire

(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.

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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

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

Experts discuss psychiatrys role in the advancement of AI technologies at the 2022 APA Annual Meeting.

CONFERENCE REPORTER

Artificial intelligence (AI) is here to stay, and its really very important for the psychiatric field to take a leading role in developing it.

P. Murali Doraiswamy, MBBS, FRCP, of the Duke University School of Medicine discussed some of the latest developments in AI and its current and potential applications for psychiatry at the 2022 American Psychiatric Association (APA) Annual Meeting. He was joined in a panel discussion by Robert M. Califf, MD, MACC, commissioner of food and drugs for the US Food and Drug Administration (FDA), and moderator Samantha Boardman, MD, psychiatrist and author of Everyday Vitality: Turning Stress Into Strength.

If I was a computer programmer describing the unmet needs in the mental health field, I would use a formula that would go something like this: 40, 40, 30, 0. This means only 40% of people who need care get access to the care they want; of those, only 40% get optimal care; of those, only 30% achieve remission; and 0% of people get preventive care in terms of resilience, Doraiswamy said. Thats the problem in this fieldand the hope and promise is that AI and technology can help with some of this.

Doraiswamy noted that $4 billion has been invested in AI, which has been doubling every 3 months for the past few years and transforming multiple fields, including psychiatry. In psychiatry, digital health coaching, stigma reduction, triage and suicide prediction, clinical decision support, and therapeutic apps have already transformed the field, with therapeutic wearables, robots and virtual reality avatars, protocol standardization, QC and practice management, and population forecasting on the horizon.

Mental health and wellness apps are particularly lucrative, with more than 5000 such apps currently on the market and around 250 million individuals having accessed them in the past 2-and-a-half years. Although some are FDA-approvedsuch as Endeavor Rx for the treatment of pediatric attention-deficit/hyperactivity disorder (ADHD) and Somryst for the treatment of chronic insomniait is important to note that many are neither approved nor overseen by the FDA unless they make a disease claim. Many also provide inaccurate information, send data to third parties, and are based on black box algorithms rather than randomized controlled trials. However, recent data shows that 82% of individuals believe robots can support their mental health better than humans can, as robots are unbiased and judgment-free, and able to provide them with quick answers.

In addition to this, clinicians have cited a number of potential harms of AI, including diagnostic and treatment errors; a loss in creative clinical thinking; greater risks of dehumanization and the jeopardization of the therapeutic process due to a lack of empathy; and less privacy and more fatalism in general. Clinicians also worry that the process for the machine to reach a diagnosis could turn into a black box process, and that, if time saved is used by administrators to increase patient loads, it might lead to greater clinician burnout.

Another potential concern for clinicians is job security in light of the fully automated psychiatrist, which is another form of AI in development. Doraiswamy emphasized that a development like this should be regarded as an enhancement to psychiatric practice rather than as a replacement. This is not going to replace you, Doraiswamy said. This is a personal assistant thats going to be sitting in the waiting room doing the initial interview intake, get all the intake ready, and then summarize this information and have it ready for you so that you can make a diagnostic assessment.

Clinicians have also cited a number of benefits of AI, including better outcomes through more standardized protocols and the elimination of human error; less bias due to race or gender; and scalability of treatment. They say AI can also encourage patients to answer truthfully and accept support more objectively; use big data more efficiently than humans; provide practical guidance for trainees; and help elucidate etiologies of diseases that are currently not well understood.

The evidence for the predictive benefits of AI for psychiatry is also growing. Recent research has found support that AI may be able to detect Alzheimer disease 5 years before diagnosis and predict future depressive episodes and risk of suicide, and that machine learning may be able to predict a mental health crisis using only data from electronic health records. By and large, this shows you the promise, Doraiswamy said. And, as DeepMindwhich is owned by Googlerecently released a general-purpose AI agent, its probable that you could have 1 AI program that could help with all 159 diseases in the DSM-5, Doraiswamy said.

In order to maximize the potential benefits of AI and ensure that psychiatry leads this revolution, Doraiswamy recommended that the field develop clinical practice guidelines; provide proper education and training; ensure that cases are relevant and human centered; advocate for equitable and accountable AI; implement QI methods into workflow; create benchmarks for trusted and safe apps; and work with payers to develop appropriate reimbursement.

We need to step back and acknowledge that digitation in our society and broad access to the internet are having profound effects that we dont yet understand, and that as we develop technologies with the plasticity that these technologies have, as opposed to traditional medical devices, you can change the software very quickly, Califf said. Its a tremendous potential benefit, but it also carries very specific risks that we need to be aware of.

Some very reasonable people might argue that AI and psychiatry dont belong even in the same sentencethat AI should play no role whatsoever in mental health care, and that the psychotherapeutic relationship is sacrosanct, Boardman concluded. But in a world where there are so many unmet mental health needs, I think theres a very good argument that AI can not only improve care and diagnostics and increase access, but also reduce stigma and even flag potential issues and symptoms before they appear and reduce burnout among professionals.

Read more from the original source:
Leading the Artificial Intelligence Revolution - Psychiatric Times

You've probably heard for years that the workforce would be supplanted by robots. AI has changed several roles, such as using self-checkouts, ATMs, and customer support chatbots. The goal is not to scare people, but to highlight the fact that AI is constantly altering lives and executing activities to replace the human workforce. At the same time, technological advancements are producing new career prospects. AI is predicted to increase the demand for professionals, particularly in robotics and software engineering. As a result, AI has the potential to eliminate millions of current occupations while also creating millions of new ones.

Among the many concerns that AI raises is the possibility of wiping out a large portion of the human workforce by eliminating the need for manual labour. But it will simultaneously liberate humans from having to perform tedious, repetitive tasks, allowing them to focus on more complex and rewarding projects, or simply take some much-needed time off.

According to a McKinsey report, depending on the adoption scenario, automation will displace between 400 and 800 million jobs by 2030, requiring up to 375 million people to change job categories entirely.

Though the potential of AI is unimaginable it is also restricted. While it is apparent that AI will dominate the professional world on many levels. However, there can be no denial that as advanced as AI may be, it can and never will be able to replicate human consciousness that reinforces the human beings' position at the top of the food chain.

Until now, we are talking about the jobs that can be snatched as technology advances but then, the human aspects of work cannot be replaced. Let's focus on something that they cannot do. There are some jobs that only humans are capable of performing.

There are jobs that require creation, conceptualization, complex strategic planning, and dealing with unknown spaces and feelings or emotional interactions that are way beyond the expertise of an AI as of now. Let's now talk about certain skills that are irreplaceable till the human race exist.1. Empathy is unique to humans: Some may argue that animals show empathy as well, but they are not the ones taking over the jobs. Humans, unlike programmed software designed to produce a specific result, are capable of feeling emotions. It may seem contradictory, but the personal affinity between a person and an organisation is the foundation of a professional relationship. Humans need a personal connection that extends beyond the professional realm to develop trust and human connection, something that bot technology completely lacks.2. Emotional Intelligence: Though accurate, the AI is not intuitive, or culturally sensitive because that's a human trait. No matter how accurately it is programmed to carry out a task, it cannot possess the human ability to adjust to the algorithm of human intellect. For instance, reading into the situation or the face of another human. It lacks emotional intellect which makes humans capable of understanding and handling an interaction that needs emotional communication. Exactly during your customer care service, one would always prefer a human interaction to read and understand the situation than an automated machine that cannot work or help beyond the programming.

3. Creativity: Perk of being human: AI can improve productivity and efficiency by reducing errors and repetition and replacing manual jobs with intelligent automated solutions, but it cannot comprehend human psychology. Furthermore, as the world becomes more AI-enabled, humans will be able to take on increasingly innovative tasks.

4. Problem-solving outside a code: Humans can deal with unexpected uncertainty by analysing the situation; like critical thinking during complex scenarios, and adopting

There is not even the slightest doubt that AI will not drive the future. To make AI work, humans need to be creative, insightful, and contextually aware. The reason for this is straightforward: humans will continue to provide value that machines cannot duplicate.

(Amarvijayy Taandur, CBO - BYLD group for Crucial Learning)

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Four skills that won't be replaced by Artificial Intelligence in the future - Economic Times