Category Archives: Artificial Intelligence

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AI is becoming strategic for many companies across the world.The technology can be transformative for just about any part of a business.

But AI is not easy to implement.Even top-notch companies have challenges and failures.

So what can be done?Well, one strategy is to provide AI education to the workforce.

If more people are AI literate and can start to participate and contribute to the process, more problemsboth big and smallacross the organization can be tackled, said David Sweenor, who is the Senior Director of Product Marketing at Alteryx.We call this the Democratization of AI and Analytics. A team of 100, 1,000, or 5,000 working on different problems in their areas of expertise certainly will have a bigger impact than if left in the hands of a few.

Just look at Levi Strauss & Co.Last year the company implemented a full portfolio of enterprise training programsfor all employees at all levelsfocused on data and AI for business applications.For example, there is the Machine Learning Bootcamp, which is an eight-week program for learning Python coding, neural networks and machine learningwith an emphasis on real-world scenarios.

Our goal is to democratize this skill set and embed data scientists and machine learning practitioners throughout the organization, said Louis DeCesari, who is the Global Head of Data, Analytics, and AI at Levi Strauss & Co.In order to achieve our vision of becoming the worlds best digital apparel company, we need to integrate digital into all areas of the enterprise.

Granted, corporate training programs can easily become a waste.This is especially the case when there is not enough buy-in at the senior levels of management.

It is also important to have a training program that is more than just a bunch of lectures.You need to have outcomes-based training, said Kathleen Featheringham, who is the Director of Artificial Intelligence Strategy at Booz Allen.Focus on how AI can be used to push forward the mission of the organization, not just training for the sake of learning about AI. Also, there should be roles-based training.There is no one-size-fits-all approach to training, and different personas within an organization will have different training needs.

AI training can definitely be daunting because of the many topics and the complex concepts.In fact, it might be better to start with basic topics.

A statistics course can be very helpful, said Wilson Pang, who is the Chief Technology Officer at Appen.This will help employees understand how to interpret data and how to make sense of data. It will equip the company to make data driven decisions.

There also should be coverage of how AI can go off the rails.There needs to be training on ethics, said Aswini Thota, who is a Principal Data Scientist at Bose Corporation.Bad and biased data only exacerbate the issues with AI systems.

For the most part, effective AI is a team sport.So it should really involve everyone in an organization.

The acceleration of AI adoption is inescapablemost of us experience AI on a daily basis whether we realize it or not, said Alex Spinelli, who is the Chief Technology Officer at LivePerson.The more companies educate employees about AI, the more opportunities theyll provide to help them stay up-to-date as the economy increasingly depends on AI-inflected roles. At the same time, nurturing a workforce thats ahead of the curve when it comes to understanding and managing AI will be invaluable to driving the companys overall efficiency and productivity.

Tom (@ttaulli) is an advisor/board member to startups and the author of Artificial Intelligence Basics: A Non-Technical Introduction, The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems and Implementing AI Systems: Transform Your Business in 6 Steps. He also has developed various online courses, such as for the COBOL.

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Artificial Intelligence: Should You Teach It To Your Employees? - Forbes

The AAMC submitted a letter to the White House Office of Science and Technology Policy (OSTP) and the National Science Foundation (NSF) on Sept. 1 in response to a request for information (RFI) geared toward developing a shared, national artificial intelligence (AI) research infrastructure that is referred to as the National Artificial Intelligence Research Resource (NAIRR).

The RFI will inform the work of the NAIRR Task Force, which has been directed by Congress to develop a first-of-its-kind AI infrastructure that provides AI researchers and students across scientific disciplines with access to computational resources, high-quality data, educational tools, and user support.

In its comments, the AAMC expressed strong support for Congress prioritization of AI, which has tremendous potential to advance human health and usher in a new era of biomedicine. The AAMC also commended the aspirations of the OSTP and the NSF to develop an inclusive AI infrastructure that allows all of America's diverse AI researchers to fully participate in exploring innovative ideas for advancing AI, including communities, institutions, and regions that have been traditionally underserved.

The letter outlined strategies on how the NAIRR should reinforce principles of ethical and responsible research and development of AI. In particular, the AAMC underscored the necessity of building a NAIRR that identifies and addresses systemic inequities at the interface of AI and biomedicine, mitigates bias by promoting representative datasets and algorithms, provides users with a data management and sharing plan that promotes community engagement and transparency, and fosters a diverse AI workforce and leadership.

Given the vast amounts of data, industries, and applications that will converge with the NAIRR, the AAMC also noted the importance of a multisector approach for identifying, researching, and mitigating bias, discrimination, health inequities, and social determinants of health all components that currently preclude the formation of an equitable AI framework that benefits all communities equally.

Finally, the AAMC recommended that the NAIRR partner with diverse communities in the development of this framework, thereby culminating a diverse expertise and fostering community trust. On Aug. 18, the OSTP and the NSF extended the RFIs public comment period by one month to Oct. 1, providing further opportunity for researchers and academic institutions to respond.

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AAMC Comments on National Artificial Intelligence Initiative - AAMC

Artificial intelligence in filmmaking might sound futuristic, but we have reached this place. Technology is already making a significant impact on film production.

Today, most of the outperforming movies that come under the visual effects category are using machine learning and AI for filmmaking. Significant pictures like The Irishman and Avengers: Endgame are no different.

It wont be a wonder if the next movie you watch is written by AI, performed by robots, and animated and rendered by a deep learning algorithm.

But why do we need artificial intelligence in filmmaking? In the fast-moving world, everything has relied on technology. Integrating artificial intelligence and subsequent technologies in film production will help create movies faster and obtain more income. Besides, employing technology will also ease almost every task in the film industry.

Writing scripts

Artificial intelligence writes a story is what happens here. Humans can imagine and script amazing stories, but they cant assure that it will perform well in the theatres. Fortunately, AI can. Machine learning algorithms are fed with large amounts of movie data, which analyses them and comes up with unique scripts that the audience love.

Simplifying pre-production

Pre-production is an important but stressful task. However, AI can help streamline the process involved in pre-production. AI can plan schedules according to actors and others timing, and find apt locations that will go well with the storyline.

Character making

Graphics and visual effects never fail to steal peoples hearts. Digital domain applied machine learning technologies are used to design amazing fictional characters like Thanos of Avengers: Infinity War.

Subtitle creation

Global media publishing companies have to make their content suitable for viewers from different regions to consume. In order to deliver video content with multiple language subtitles, production houses can use AI-based technologies like Natural language generation and natural language processing.

Movie Promotion

To confirm that the movie is a box-office success, AI can be leveraged in the promotion process. AI algorithm can be used to evaluate the viewer base, the excitement surrounding the movie, and the popularity of the actors around the world.

Movie editing

In editing feature-length movies, AI supports the film editors. With facial recognition technology, an AI algorithms can recognize the key characters and sort certain scenes for human editors. By getting the first draft done quickly, editors can focus on scenes featuring the main plot of the script.

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Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

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Artificial Intelligence in Film Industry is Sophisticating Production - Analytics Insight

Sep. 10, 2021Updated: Sep. 10, 2021 2:41p.m.

An IBM researcher holds a silicon wafer with embedded IBM Telum chips designed to maximize artificial intelligence capabilities. The chips were developed at Albany Nanotech and made in partnership with Samsung. The Albany area was recent cited by the Brookings Institution for having the potential to create an AI sector.

ALBANY The Capital Region is one of 87 "potential adoption centers" in the United States for companies and researchers focused on the use of artificial intelligence, or AI, according to a new report from the Brookings Institution, a left-leaning think tank. The San Francisco Bay area is No. 1 in AI, while other upstate cities, Buffalo, Rochester and Syracuse, were also listed as potential adoption centers.

The Center for Economic Growth in Albany highlighted the Brookings list as part of its own report recently published on AI research and development in the Capital Region at local universities and at companies such as IBM and General Electric.

Larry Rulison has been a reporter for the Albany Times Union since 2005. Larry's reporting for the Times Union has won several awards for business and investigative journalism from the New York State Associated Press Association and the New York News Publishers Association. Contact him at 518-454-5504 or lrulison@timesunion.com.

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Region's AI sector has potential according to think tank - Times Union

Cybercriminals are using artificial intelligence (AI) to evolve the sophistication of attacks at a rapid pace. In response, an increasing number of organisations are also adopting the technology as part of their cybersecurity strategies. According to research conducted in Mimecasts State of Email Security Report 2021, 39 per cent of organisations are utilising AI to bolster their email defences.

Although were still in the early phases of these technologies and their application to cybersecurity, this is a rising trend. Businesses using advanced technologies such as AI and layered email defences, while also regularly training their employees in attack-resistant behaviours, will be in the best possible position to sidestep future attacks and recover quickly.

Mimecast is integrating AI capabilities to help halt some of cybersecuritys most pervasive threats. Take the use of tracking pixels in emails, for example, which both BBC and ZDNet have called endemic. Spy trackers embedded in emails have become ubiquitous often by marketers but also, increasingly, by cybercriminals looking to gather information to weaponise highly targeted business email compromise attacks.

Mimecasts CyberGraph uses machine learning, a subset of AI, to block these hard-to-detect email threats, thus limiting reconnaissance and mitigating human error. CyberGraph disarms embedded trackers and uses machine learning and identity graph technologies to detect anomalous malicious behaviour. Because the AI is continually learning, it requires no configuration, thus lessening the burden on IT teams and reducing the likelihood of unsafe misconfiguration. Plus, as an add-on to Mimecast Email Security, CyberGraph offers differentiated capability integrated into an existing secure email gateway, streamlining your email security strategy.

AI is here, and here to stay. Although its use is not a silver bullet, theres a strong case for it in the future of cybersecurity. Mimecast CyberGraph combines with many other layers of protection. It embeds colour-coded warning banners in emails to highlight detected risks, and it solicits user feedback. This feedback strengthens the machine learning model and can update banners across all similar emails to highlight the new risk levels.

As more cyber resilience strategies begin to adopt AI, it will be vital that people and technology continue to inform one another to provide agile protection against ever-evolving threat landscapes. Innovations such as CyberGraph provide evidence that AI has a promising value proposition in cybersecurity.

Duncan Mills is the senior product marketing manager at Mimecast

This article was originally published in the Summer2021 issue of The Record. To get future issues delivered directly to your inbox, sign up for a free subscription.

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Artificial intelligence is the future of cybersecurity - Technology Record

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Rev Neurosci. 2021 Sep 10. doi: 10.1515/revneuro-2021-0101. Online ahead of print.

ABSTRACT

Artificial intelligence (AI) is a branch of computer science with a variety of subfields and techniques, exploited to serve as a deductive tool that performs tasks originally requiring human cognition. AI tools and its subdomains are being incorporated into healthcare delivery for the improvement of medical data interpretation encompassing clinical management, diagnostics, and prognostic outcomes. In the field of neuroradiology, AI manifested through deep machine learning and connected neural networks (CNNs) has demonstrated incredible accuracy in identifying pathology and aiding in diagnosis and prognostication in several areas of neurology and neurosurgery. In this literature review, we survey the available clinical data highlighting the utilization of AI in the field of neuroradiology across multiple neurological and neurosurgical subspecialties. In addition, we discuss the emerging role of AI in neuroradiology, its strengths and limitations, as well as future needs in strengthening its role in clinical practice. Our review evaluated data across several subspecialties of neurology and neurosurgery including vascular neurology, spinal pathology, traumatic brain injury (TBI), neuro-oncology, multiple sclerosis, Alzheimers disease, and epilepsy. AI has established a strong presence within the realm of neuroradiology as a successful and largely supportive technology aiding in the interpretation, diagnosis, and even prognostication of various pathologies. More research is warranted to establish its full scientific validity and determine its maximum potential to aid in optimizing and providing the most accurate imaging interpretation.

PMID:34506699 | DOI:10.1515/revneuro-2021-0101

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Current uses, emerging applications, and clinical integration of artificial intelligence in neuroradiology - DocWire News

Applications of AI And Machine Learning In Computer Science and Electrical Engineering

Technologically, we are evolving with every passing day. Progress in the field of Artificial intelligence and machine learning has transformed our lives for the better. Today, these magnificent technologies are used to optimize systems and meet the desired organizations goals. AI and machine learning not only boost the performance of the system but also address the problems of the business like never before. Additionally, problems are addressed efficiently and faster than before. All in all, implementing the latest applications of AI and machine learning might end up being a path for achieving greater heights. Computer engineering systems and electrical engineering systems generate huge volumes of data. Thus, we can apply data mining to discover new relationships in these systems. With the advent of deep neural networks thanks to the advancement in technology, we can learn new mappings between inputs and output of these systems. On that note, have a look at some of the greatest applications of AI and machine learning in the field of Computer engineering and electrical engineering that have simplified our lives.

Power systems

One of the best applications of AI when it comes to computer engineering has been on power systems. Right from identifying malfunctions to forecasting, AI has covered it all. Artificial intelligence has done a magnificent job in reducing the workload of human operators by taking up tasks such as data processing, routine maintenance, training, etc.

Application of Artificial intelligence in Electrical Equipment

First things first, we all know how complex the electrical equipment structure is. In reality, it not only needs knowledge pertaining to electronics, circuits, electromagnetic fields, motors, automation, etc. but also the necessity to understand the generators, sensors and other components of the role and mechanism. It is here that AI turns out to be no less than a saviour. Through programming and operation by computer technology, AI can realize the automatic operation of electrical equipment and replace human labour as well, thereby reducing the labour cost to a large extent. Additionally, Artificial intelligence technology greatly improves the speed and precision of the work.

Fault diagnosis

Artificial intelligence can be used in the logic of fuzzy neural network expert systems timely. With this, it is not only possible to accurately detect the faults, but also used to determine the cause of the failure, type and location of thefailure, and timely control of fault repair.

More secure systems

With the help of advanced search algorithms, Artificial intelligence and machine learning, identifying potential threats and data breaches in real-time has become easier than ever. Well, this is not it there is more to this. These advanced technologies also provide the necessary solutions to avoid those issues in the future. Well, there is no denying that when it comes to computer science, data security becomes way more relevant, right?

Server optimization

We all know that hosting servers have millions of inbound requests on a day-to-day basis. However, a point of concern is that due to the continuous flow of queries, some of these servers may end up slowing down and become unresponsive. Well, Artificial intelligence to the rescue it is! AI holds the potential of optimizing the host server and enhancing the operations, thereby boosting customer service.

What everything boils down to is the fact that AI and machine learning are changing many sectors, particularly IT/computer and electrical engineering because of the amount of data sets it can process at greater speeds and ability to learn faster than the human brain.

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Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

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Applications of AI And Machine learning In Computer Science and Electrical Engineering - Analytics Insight

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Radiat Oncol. 2021 Sep 9;16(1):175. doi: 10.1186/s13014-021-01896-1.

ABSTRACT

BACKGROUND: Contour delineation, a crucial process in radiation oncology, is time-consuming and inaccurate due to inter-observer variation has been a critical issue in this process. An atlas-based automatic segmentation was developed to improve the delineation efficiency and reduce inter-observer variation. Additionally, automated segmentation using artificial intelligence (AI) has recently become available. In this study, auto-segmentations by atlas- and AI-based models for Organs at Risk (OAR) in patients with prostate and head and neck cancer were performed and delineation accuracies were evaluated.

METHODS: Twenty-one patients with prostate cancer and 30 patients with head and neck cancer were evaluated. MIM Maestro was used to apply the atlas-based segmentation. MIM Contour ProtgAI was used to apply the AI-based segmentation. Three similarity indices, the Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean distance to agreement (MDA), were evaluated and compared with manual delineations. In addition, radiation oncologists visually evaluated the delineation accuracies.

RESULTS: Among patients with prostate cancer, the AI-based model demonstrated higher accuracy than the atlas-based on DSC, HD, and MDA for the bladder and rectum. Upon visual evaluation, some errors were observed in the atlas-based delineations when the boundary between the small bowel or the seminal vesicle and the bladder was unclear. For patients with head and neck cancer, no significant differences were observed between the two models for almost all OARs, except small delineations such as the optic chiasm and optic nerve. The DSC tended to be lower when the HD and the MDA were smaller in small volume delineations.

CONCLUSIONS: In terms of efficiency, the processing time for head and neck cancers was much shorter than manual delineation. While quantitative evaluation with AI-based segmentation was significantly more accurate than atlas-based for prostate cancer, there was no significant difference for head and neck cancer. According to the results of visual evaluation, less necessity of manual correction in AI-based segmentation indicates that the segmentation efficiency of AI-based model is higher than that of atlas-based model. The effectiveness of the AI-based model can be expected to improve the segmentation efficiency and to significantly shorten the delineation time.

PMID:34503533 | DOI:10.1186/s13014-021-01896-1

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Evaluation of auto-segmentation accuracy of cloud-based artificial intelligence and atlas-based models - DocWire News

A problem well-diagnosed is a problem half solved.Considering all the breathless hype we hear about Artificial Intelligence (AI) today, it is easy to overlook the arguments people have raised against it.

The pace of development of these new-world technologies has attracted a fair amount of criticism. There are opponents of AI around the world who are scared of these rapidly developing technologies.

Many thinkers have been vocal in sharing their concerns about various dangers associated with AI. From robots controlling the world to lack of employment, concerns about these technologies have people wringing their hands.

There is no doubt these technologies can be put to dangerous use. I understand, fear is always real even when the reason is debatable. But we need to understand technology is always neutral.

The problem is not the pace of development of AI, but the pace of the development of humans.This criticism of entrepreneurs and developers is not from a fully aware point of view.

The criticism is against the progressive nature of technology. This criticism is against the betterment of humans.

The pace of change of humans and the pace of change of technology has been mismatched.

The game is not to stop the growth of technology or to slow down the growth of technology. But to increase the pace of the growth of humankind.

We need to move towards raising the consciousness of humans to enable them to live in this new world of AI. Human learning must keep pace with machine learning. The original intelligence of humans must outshine artificial intelligence.

We need to reform the way we educate ourselves. The formal and informal education systems must recalibrate themselves to create human beings capable of dealing with these high-tech powers.

Companies investing unbelievable amounts of money in the advancement of technology must also invest at least at par in the learning and development of employees.

But let us also keep in mind that we need to match the pace of human development with the pace of technological development.

(Edited by : Yashi Gupta)

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View: Are our fears of artificial intelligence justified? - CNBCTV18

When I was a kid, artificial intelligence meant a person had graduated college but was still a dunce. In our times, however, Artificial Intelligence means something else. If computers and cell phones were the technological legacy of the latter 20th century, Artificial Intelligence might be the technological legacy of the 21st century.

Most of us run across Artificial Intelligence regularly. When you call to make an appointment or inquire about ordering supplies on the phone, you might be talking to a computer with voice recognition technology.

Sometimes when you answer your phone and hear the friendly voice of the telemarketer who responds to your responses, it may take you a while to realize you are talking to a computer, not a real human being.

Have you ever searched for a product on the internet or at Amazon, only to see the product advertised on your Facebook page? How did they know? Artificial Intelligence. Computers follow recipes called algorithms that direct the computer to note what you have searched for and then to match that search with advertisers and put the appropriate ad on your Facebook page.

Computer algorithms are just an example of Artificial Intelligence at work.

According to academicinfluence.com, Artificial Intelligence abbreviated AI refers to computing which aims to mimic human cognitive functions like learning, problem solving, and adaptation to environmental conditions. ... Artificial Intelligence is actually an umbrella term for various areas of computing including robotics, machine learning, and Artificial neural networking (mimicking the human mind in some way)

But AI programs can make blunders; Artificial Intelligence is not always so intelligent. Take this recent account from the BBC: Facebook users who watched a newspaper video featuring black men were asked if they wanted to keep seeing videos about primates by an Artificial-Intelligence recommendation system.

Facebook told BBC News it was clearly an unacceptable error, disabled the system and launched an investigation.

"We apologize to anyone who may have seen these offensive recommendations."

Some futurists talk about singularity, a time when computers will be self-sustaining, self-improving and no longer need human help. Wikipedia defines singularity further: ... a hypothetical point in time at which technological growth becomes uncontrollable and irreversible, resulting in unforeseeable changes to human civilization ... (with( runaway reaction of self-improvement cycles rapidly causing an explosion in Intelligence (that) far surpasses all human intelligence.

Many people are afraid that computers and robots will eventually take over the world, and humans will become servants to these more intelligent pieces of technology. Not everyone is on board with such a doomsday picture.

Gotquestions.org makes some good points about AI: If a person defines Intelligence in a way that eliminates concepts such as morality, emotion, empathy, humor, relationship, and so forth, then the phrase Artificial Intelligence is not so meaningful. This is a particularly important point to keep in mind when discussing strategy games ... in which computers often defeat even the greatest human masters ... the program that bests a human in a strategy game is designed specifically for playing that game. It might win, but the human can then leave the room and do ... things that the machine cannot do. The software that allows the machine to succeed in a trivia game cant tell you how to tie your shoes. Or make a sandwich. Or draw a flower. Or write a limerick. Nor can it comfort a sick child, pretend to be a character in a play, or watch a movie and later explain the plot to someone else. The truth is that those purpose-built AI computers are markedly less intelligent than the humans whom they defeated in narrow contests.

So where will this go? Let me illustrate. Many children today cannot print neatly; others cannot solve basic math problems apart from a calculator. Fortunately, many can do both of the above.

This divide will continue to deep as AI continues to mushroom. Some of us will make the effort to develop and nurture all aspects of our humanity, while others will allow our own humanity to atrophy and take the path of resistance, always relying on AI. The robots will not take over, but some of us will surrender portions of our humanity to them.

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Ed Vasicek: Not all of us are on board with robots taking over the world - Kokomo Tribune