Mediaboss Marketing

How should IT leaders and professionals go about selecting and delivering the technology required to deliver the storied marvels of artificial intelligence and machine learning? AI and ML require having many moving parts in their right places, moving in the right direction, to deliver on the promise these technologies bring -- ecosystems, data, platforms, and last, but not least, people.

Is there a way for IT leaders to be proactive about AI and ML without ruffling and rattling an organization of people who want the miracles of AI and ML delivered tomorrow morning? The answer is yes.

The authors of a recent report from MIT Sloan Management Review and SAS advocates a relatively new methodology to successfully accomplish the delivery AI and ML to enterprises called "ModelOps." While there a lot of "xOps" now entering our lexicon, such as MLOps orAIOps, ModelOps is more "mindset than a specific set of tools or processes, focusing on effective operationalization of all types of AI and decision models."

That's because in AI and ML, models are the heart of the matter, the mechanisms that dictate the assembly of the algorithms, and assure continued business value. ModelOps, which is short for :model operationalization, "focuses on model life cycle and governance; intended to expedite the journey from development to deployment -- in this case, moving AI models from the data science lab to the IT organization as quickly and effectively as possible."

In terms of operationalizing AI and ML, "a lot falls back on IT," according to Iain Brown, head of data science for SAS, U.K. and Ireland, who is quoted in the report. "You have data scientists who are building great innovative things. But unless they can be deployed in the ecosystem or the infrastructure that exists -- and typically that involves IT - - there's no point in doing it. The data science community and AI teams should be working very closely with IT and the business, being the conduit to join the two so there's a clear idea and definition of the problem that's being faced, a clear route to production. Without that, you're going to have disjointed processes and issues with value generation."

ModelOps is a way to help IT leaders bridge that gap between analytics and production teams, making AI and ML-driven lifecycle "repeatable and sustainable," the MIT-SAS report states. It's a step above MLOps or AIOps, which "have a more narrow focus on machine learning and AI operationalization, respectively," ModelOps focuses on delivery and sustainability of predictive analytics models, which are the core of AI and ML's value to the business. ModelOps can make a difference, the report's authors continue, "because without it, your AI projects are much more likely to fail completely or take longer than you'd like to launch. Only about half of all models ever make it to production, and of those that do, about 90% take three months or longer to deploy."

Getting to ModelOps to manage AI and ML involves IT leaders and professionals pulling together four key elements of the business value equation, as outlined by the report's authors.

Ecosystems: These days, every successful technology endeavor requires connectivity and network power. "An AI-ready ecosystem should be as open as possible, the report states. "Such ecosystems don't just evolve naturally. Any company hoping to use an ecosystem successfully must develop next-generation integration architecture to support it and enforce open standards that can be easily adopted by external parties."

Data:Get to know what data is important to the effort. "Validate its availability for training and production. Tag and label data for future usage, even if you're not sure yet what that usage might be. Over time, you'll create an enterprise inventory that will help future projects run faster."

Platforms: Flexibility and modularity -- the ability to swap out pieces as circumstance change -- is key. The report's authors advocate buying over building, as many providers have already worked out the details in building and deploying AI and ML models. "Determine your cloud strategy. Will you go all in with one cloud service provider? Or will you use different CSPs for different initiatives? Or will you take a hybrid approach, with some workloads running on-premises and some with a CSP? : Some major CSPs typically offer more than just scalability and storage space, such as providing tools and libraries to help build algorithms and assisting with deploying models into production."

People: Collaboration is the key to successful AI and ML delivery, but it's also important that people have a sense of ownership over their parts of the projects."Who owns the AI software and hardware - the AI team or the IT team, or both? This is where you get organizational boundaries that need to be clearly defined, clearly understood, and coordinated." Along with data scientists, a group that is just as important to ModelOps is data engineers, who bring "significant expertise in using analytics and business intelligence tools, database software, and the SQL data language, as well as the ability to consistently produce clean, high-quality, ethical data."

Original post:
At last, a way to build artificial intelligence with business results in mind: ModelOps - ZDNet

Reflation trade has been pummelled after the Federal Reserve unexpectedly signalled a shift in its stance on inflation, and, European Central Bank executive Fabio Panetta says the introduction of a digital euro would boost consumers privacy. Plus, the FTs innovation editor, John Thornhill, talks about the new season of the Tech Tonic podcast and its main focus, artificial intelligence.

Reflation trades pummelled as Fed shift resets markets

https://www.ft.com/content/2fa0c907-f597-49b2-a08d-35249d1d5a9f

Digital euro will protect consumer privacy, ECB executive pledges

https://www.ft.com/content/e59e5d61-043a-4293-8692-f8267e5984c2?

Tech Tonic Season 2

https://www.ft.com/tech-tonic

Today's Clubhouse discussion on artificial intelligence

https://www.clubhouse.com/join/FinancialTimes/MLICXXgQ/PAwJ017M

See acast.com/privacy for privacy and opt-out information.

A transcript for this podcast is currently unavailable, view our accessibility guide.

Go here to see the original:
How artificial intelligence is reshaping the world - Financial Times

Search for Extraterrestrial Intelligence (SETI) astronomer Seth Shostak (pictured) confesses that these are exciting times for alien hunters like himself, what with the Pentagons anticipated July 25 report on unidentified aerial phenomena. Still, he doesnt expect any big revelations: I think its overwhelmingly likely that aliens are present in our galaxy. But I dont believe theyre hanging out in our airspace. Not now, and not in historic times.

On the other hand, he goes on to say, every third star in our galaxy could host an Earth-like planet so the odds are we are not alone. Few life forms on Earth resemble humans, so why should extraterrestrials?

But if we are not alone, what would ET be like? A gaseous cloud? A plant? Pure information?

Shostak argues, somewhat daringly, that ETs who traveled to Earth would probably not be alive in a conventional sense. In fact, given the immense interstellar distances, not being alive might be the only way they could get here. They might be artificial intelligences:

Such leisurely trips arent going to appeal to biological passengers who will die long before their destination is reached. Machines, on the other hand, wont complain if theyre cooped up in a spaceship for tens of thousands of years. They dont require food, oxygen, sanitation or entertainment. And they dont insist on a round-trip ticket.

Artificial intelligence aliens may not be as appealing as those who are warm-blooded and squishy, but we shouldnt get hung up on an anthropocentric viewpoint. Researchers who work in AI estimate that machines able to beat humans on an IQ test will emerge from the labs by mid-century. If we can do it, some extraterrestrials will have already done it.

Shostak sounds overly optimistic about artificial intelligence beating humans on an IQ test. Its helpful to remember that computers only compute. Many thought processes are not forms of computing. As tech philosopher George Gilder points out, AI can win if the map is the territory (think chess) because then pure computation can win. But a map of Earth is not the territory and non-computational methods of thought are essential.

Of greater concern would be its intentions. Most sci-fi stories postulate that visitors would be noxious, arriving with a primal urge to obliterate Los Angeles or London. Frankly, if thats whats on their mechanical minds, its probably impossible to keep them at bay. Chimps couldnt outsmart humans in any serious confrontation. Likewise, devices who can manage a trip to Earth will have the capability to do whatever they wish once they get here.

But wait. If they are artificial intelligences, they wouldnt have any desires at all. Someone might have programmed them to do thus-and-such. But obliterating the newly discovered research subjects, after all that time and trouble, seems like an unlikely program.

Waiting for the Pentagons report is a fun time and we are all entitled to our speculations.

You may also wish to read: Astronomer bets a cup of coffee that well encounter ET by 2036 Seth Shostak points to the increase in the number of exoplanets identified and the increase in computing power. One problem is that signals to and from exoplanets may take years. It takes up to 21 minutes for a signal from Earth to reach even Mars.

and

Seven reasons (so far) why the aliens never show up. Some experts think they became AI and some that they were killed by their AI but others say they never existed. Whos most likely right? Indeed, where are they? A flurry of explanations creates some great sci-fi.

View original post here:
ET Expert Thinks That ET Is Probably AI (Artificial Intelligence) - Walter Bradley Center for Natural and Artificial Intelligence

AI, Artificial Intelligence concept,3d rendering,conceptual image.

The Biden administration has recently set into action its initiative on AI (Artificial Intelligence).This is part of legislation that was passed last year and included a budget of $250 million (for a period of five years).The goals are to provide easier access to the troves of government data as well as provide for advanced systems to create AI models.

No doubt, this effort is a clear sign of the strategic importance of the technology.It is also a recognition that the U.S. does not want to fall behind other nations, especially China.

The AI task force has 12 distinguished members who are from government, private industry and academia.This diversity should help provide for a smarter approach.

But the focus on data will also be critical. In areas of social importance such as housing, healthcare, education or other social determinants, the government is the only central organizer of data, said Dr. Trishan Panch, who is the co-founder of Wellframe.As such, if AI is going to deliver gains in these areas, the government has to be involved.

Yet there will certainly be challenges.Lets face it, the U.S. government often moves slowly and is burdened with various levels of local, state and federal authorities.

To achieve the initiatives vision, government entities will need to go beyond sharing best practices and figure out how to share more data across departments, said Justin Borgman, who is the CEO of Starburst.For instance, expanding open data initiatives which today are largely siloed by departments, would greatly improve access to data. That would give Artificial Intelligence systems more fuel to do their jobs.

If anything, there will be a need for a different mindset from the government.And this could be a heavy lift.Based on my experience in the public sector, the major challenge for the government is addressing the Missing Middle, said Jon Knisley, who is the Principal of Automation and Process Excellence at FortressIQ. There are a number of very advanced programs on one end, and then there are a lot of emerging programs on the other end. The greatest opportunity lies in closing that gap and driving more adoption. To be successful, there should be a focus as much as possible on applied AI.

But the government initiative can do something that has been difficult for the private sector to achievethat is, to help reskill the workforce for AI.This is perhaps one of the biggest challenges for the U.S.

The question is: How do we create a large AI data science force that is integrated across every industry and department in the US?, said Judy Lenane, who is the Chief Medical Officer at iRhythm.To start, well need to begin AI curriculum early and encourage its growth in order to build a comprehensive workforce. This will be especially critical for industries that are currently behind in technological adoption, such as construction and infrastructure, but it also needs to be accessible.

In the meantime, the Biden AI effort will need to deal with the complex issues of privacy and ethics.

Presently there is significant resistance on this subject given that most consumers feel that their privacy has been compromised, said Alice Jacobs, who is the CEO of convrg.ai.This is the result of a lack of transparency around managing consents and proper safeguards to ensure that data is secure. We will only be able to be successful if we can manage consents in a way where the consumer feels in control of their data.Transparent unified consent management will be the path forward to alleviate resistance around data access and can provide the US a competitive advantage in this data and AI arms race.

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 and Python programming languages.

More here:
Bidens AI Initiative: Will It Work? - Forbes

Applications of artificial intelligence are growing every day in different sectors. There are numerous instances of AI applications in healthcare. The AI that occurs in hearing aids has actually been going on for years and the following is about how it happened. Hearing aids used to be relatively simple, he notes, but when hearing aids introduced a technology known as wide dynamic range compression (WDRC), the devices actually began to make a few decisions based on what is heard. For hearing aids to work effectively, they need to adapt to a persons individual hearing needs as well as all sorts of background noise environments. AI, machine learning, and neural networks are very good techniques to deal with such a complicated, nonlinear, multi-variable problem.

Researchers have been able to accomplish a lot with AI to date when it comes to improving hearing. For instance, researchers at the Perception and Neurodynamics Laboratory (PNL) at the Ohio State University trained a DNN to distinguish speech from other noise (such as humming and other background conversations). DeLiang Wang, professor of computer science and engineering at Ohio State University, in IEEE Spectrum has further explained People with hearing impairment could decipher only 29% of words muddled by babble without the program, but they understood 84% after the processing,

In recent years, major hearing aid manufacturers have been adding AI technology to their premium hearing aid models. For example, Widexs Moment hearing aid utilizes AI and machine learning to create hearing programs based on a wearers typical environments. Recently, Oticon introduced its newest hearing aid device, Oticon More, the first hearing aid with an onboard deep neural network. Oticon More has decided 12 million-plus real-life sounds so that people wearing it can better understand speech and the sounds around them. In a crowded place, Oticon Mores neural net receives a complicated layer of sounds, known as input. The DNN gets to work, first scanning and extracting simple sound elements and patterns from the input. It builds these element-powered her to recognize and make sense of whats happening. Lastly, the hearing aids then make a decision on how to balance the sound scene, making sure the output is clean and ideally balanced to the persons unique type of hearing loss. Speech and other sounds in the environment are complicated acoustic waveforms, but with unique patterns and structures that are exactly the sort of data deep learning is designed to analyze.

Hearing aids range widely in price, and some at the lower end have fewer AI-driven bells and whistles. Some patients may not need all the features, like the people who live alone or rarely leave the house find themselves in crowded scenarios often, for instance, might not benefit from the functionality found in higher-end models.

But for anyone who is out and about a lot, especially in situations where there are big soundscapes, AI-powered features allow for an improved hearing experience. The improvement of memory can be measured in a lot of more natural cater is memory recall. Its not that the hearing aids like Oticon More literally improve a persons memory, but artificial intelligence helps people spend less time trying to make sense of the noise around them, a process known as listening effort. When the listening effort is more natural, a person can focus more on the conversation and all the nuances conveyed within. So, the use of AI in hearing aids would help the brain work in a more natural way.

Share This ArticleDo the sharing thingy

More:
Use of Artificial Intelligence in the Making of Hearing Aids - Analytics Insight