Artificial intelligence (AI) and machine learning (ML) are some of the most hyped enterprise technologies and have caught the imagination of boards, with the promise of efficiencies and lower costs, and the public, with developments such as self-driving cars and autonomous quadcopter air taxis.

Of course, the reality is rather more prosaic, with firms looking to AI to automate areas such as online product recommendations or spotting defects on production lines. Organisations are using AI in vertical industries, such as financial services, retail and energy, where applications include fraud prevention and analysing business performance for loans, demand prediction for seasonal products and crunching through vast amounts of data to optimise energy grids.

All this falls short of the idea of AI as an intelligent machine along the lines of 2001: A Space Odysseys HAL. But it is still a fast-growing market, driven by businesses trying to drive more value from their data, and automate business intelligence and analytics to improve decision-making.

Industry analyst firm Gartner, for example, predicts that the global market for AI software will reach US$62bn this year, with the fastest growth coming from knowledge management. According to the firm, 48% of the CIOs it surveyed have already deployed artificial intelligence and machine learning or plan to do so within the next 12 months.

Much of this growth is being driven by developments in cloud computing, as firms can take advantage of the low initial costs and scalability of cloud infrastructure. Gartner, for example, cites cloud computing as one of five factors driving AI and ML growth, as it allows firms to experiment and operationalise AI faster with lower complexity.

In addition, the large public cloud providers are developing their own AI modules, including image recognition, document processing and edge applications to support industrial and distribution processes.

Some of the fastest-growing applications for AI and ML are around e-commerce and advertising, as firms look to analyse spending patterns and make recommendations, and use automation to target advertising. This takes advantage of the growing volume of business data that already resides in the cloud, cutting out the costs and complexity associated with moving data.

The cloud also lets organisations make use of advanced analytics and compute facilities, which are often not cost-effective to build in-house. This includes the use of dedicated, graphics processing units (GPUs) and extremely large storage volumes made possible by cloud storage.

Such capabilities are beyond the reach of many organisations on-prem offerings, such as GPU processing. This demonstrates the importance of cloud capability in organisations digital strategies, says Lee Howells, head of AI at advisory firm PA Consulting.

Firms are also building up expertise in their use of AI through cloud-based services. One growth area is AIOps, where organisations use artificial intelligence to optimise their IT operations, especially in the cloud.

Another is MLOps, which Gartner says is the operationalisation of multiple AI models, creating composite AI environments. This allows firms to build up more comprehensive and functional models from smaller building blocks. These blocks can be hosted on on-premise systems, in-house, or in hybrid environments.

Just as cloud service providers offer the building blocks of IT compute, storage and networking so they are building up a range of artificial intelligence and machine learning models. They are also offering AI- and ML-based services which firms, or third-party technology companies, can build into their applications.

These AI offerings do not need to be end-to-end processes, and often they are not. Instead, they provide functionality that would be costly or complex for a firm to provide itself. But they are also functions that can be performed without compromising the firms security or regulatory requirements, or that involve large-scale migration of data.

Examples of these AI modules include image processing and image recognition, document processing and analysis, and translation.

We operate within an ecosystem. We buy bricks from people and then we build houses and other things out of those bricks. Then we deliver those houses to individual customers, says Mika Vainio-Mattila, CEO at Digital Workforce, a robotic process automation (RPA) company. The firm uses cloud technologies to scale up its delivery of automation services to its customers, including its robot as a service, which can run either on Microsoft Azure or a private cloud.

Vainio-Mattila says AI is already an important part of business automation. The one that is probably the most prevalent is intelligent document processing, which is basically making sense of unstructured documents, he says.

The objective is to make those documents meaningful to robots, or automated digital agents, that then do things with the data in those documents. That is the space where we have seen most use of AI tools and technologies, and where we have applied AI ourselves most.

He sees a growing push from the large public cloud companies to provide AI tools and models. Initially, that is to third-party software suppliers or service providers such as his company, but he expects the cloud solution providers (CSPs) to offer more AI technology directly to user businesses too.

Its an interesting space because the big cloud providers spearheaded by Google obviously, but very closely followed by Microsoft and Amazon, and others, IBM as well have implemented services around ML- and AI-based services for deciphering unstructured information. That includes recognising or classifying photographs or, or translation.

These are general-purpose technologies designed so that others can reuse them. The business applications are frequently very use-case specific and need experts to tailor them to a companys business needs. And the focus is more on back-office operations than applications such as driverless cars.

Cloud providers also offer domain-specific modules, according to PA Consultings Howells. These have already evolved in financial services, manufacturing and healthcare, he says.

In fact, the range of AI services offered in the cloud is wide, and growing. The big [cloud] players now have models that everyone can take and run, says Tim Bowes, associate director for data engineering at consultancy Dufrain. Two to three years ago, it was all third-party technology, but they are now building proprietary tools.

Azure, for example, offers Azure AI, with vision, speech, language and decision-making AI models that users can access via AI calls. Microsoft breaks its offerings down into Applied AI Services, Cognitive Services, machine learning and AI infrastructure.

Google offers AI infrastructure, Vertex AI, an ML platform, data science services, media translation and speech to text, to name a few. Its Cloud Inference API lets firms work with large datasets stored in Googles cloud. The firm, unsurprisingly, provides cloud GPUs.

Amazon Web Services (AWS) also provides a wide range of AI-based services, including image recognition and video analysis, translation, conversational AI for chatbots, natural language processing, and a suite of services aimed at developers. AWS also promotes its health and industrial modules.

The large enterprise software and software-as-a-service (SaaS) providers also have their own AI offerings. These include Salesforce (ML and predictive analytics), Oracle (ML tools including pre-trained models, computer vision and NLP) and IBM (Watson Studio and Watson Services). IBM has even developed a specific set of AI-based tools to help organisations understand their environmental risks.

Specialist firms include H2O.ai, UIPath, Blue Prism and Snaplogic, although the latter three could be better described as intelligent automation or RPA companies than pure-play AI providers.

It is, however, a fine line. According to Jeremiah Stone, chief technology officer (CTO) at Snaplogic, enterprises are often turning to AI on an experimental basis, even where more mature technology can be more appropriate.

Probably 60% or 70% of the efforts Ive seen are, at least initially, starting out exploring AI and ML as a way to solve problems that may be better solved with more well-understood approaches, he says. But that is forgivable because, as people, we continually have extreme optimism for what software and technology can do for us if we didnt, we wouldnt move forward.

Experimentation with AI will, he says, bring longer-term benefits.

There are other limitations to AI in the cloud. First and foremost, cloud-based services are best suited to generic data or generic processes. This allows organisations to overcome the security, privacy and regulatory hurdles involved in sharing data with third parties.

AI tools counter this by not moving data they stay in the local business application or database. And security in the cloud is improving, to the point where more businesses are willing to make use of it.

Some organisations prefer to keep their most sensitive data on-prem. However, with cloud providers offering industry-leading security capabilities, the reason for doing this is rapidly reducing, says PA Consultings Howells.

Nonetheless, some firms prefer to build their own AI models and do their own training, despite the cost. If AI is the product and driverless cars are a prime example the business will want to own the intellectual property in the models.

But even then, organisations stand to benefit from areas where they can use generic data and models. The weather is one example, image recognition is potentially another.

Even firms with very specific demands for their AI systems might benefit from the expansive data resources in the cloud for model training. Potentially, they might also want to use cloud providers synthetic data, which allows model training without the security and privacy concerns of data sharing.

And few in the industry would bet against those services coming, first and foremost, from the cloud service providers.

More here:
Putting artificial intelligence and machine learning workloads in the cloud - ComputerWeekly.com

Artificial intelligence is currently being used in Edmonton to detect distracted driving as part of a research project.

On September 13, the University of Alberta launched this three-week research project to understand the prevalence of distracted drivers, specifically in Edmonton. Karim El-Basyouny, a professor in the faculty of engineering and urban traffic safety research chair at the University of Alberta, is the lead of the research team. The U of A research is in a collaboration with Acusensus, the City of Edmonton, and the Edmonton Police Service.

Since September 13, the technology has been stationed at its first location on the intersection of 79 Street and Argyll Road. According to El-Basyouny, it will be stationed there for about a week before moving to the next location, which is currently unknown. There will be a total of three different locations, one for each week during this project.

El-Basyounys research is being supported by a seed grant, making the use of Acusensus technology possible. Although the Edmonton Police Service is in collaboration with this project, the collection of data will be used solely for research, not traffic enforcement.

Edmonton is the first city in Canada to test Acusensus technology, according to Tony Parrino, the general manager for Acusensus in North America.

The data around distracted driving in Canada has been a little patchy, [and] we dont really understand how big of a problem it is what were trying to do is see if there is a better way of understanding how big of an issue [distracted driving] is, El-Basyouny explained.

The technology being used to determine the prevalence of distracted drivers is mainly AI. According to Parrino, the AI has gone through a number of training scenarios with millions of data points.

The system is radar-based with many different sensors, and four different cameras. Each camera captures something different; one captures a steep shot of the windshield, one camera is shallow in case of a phone-to-ear event, and the other two cameras are used for color context and capturing license plates. The information gathered is then given to the AI.

According to Parrino, although the AI has been trained to have maximum accuracy there is a possibility for false positives.

It is very accurate, but there are false positives 100 per cent of the images that are captured are reviewed by trained individuals [who determine if] the criteria is met for the U of A to determine that a distracted driving event has occurred, and only those are counted, Parrino said.

Although Acusensus technology is being used in Australia for traffic enforcement, according to Parrino, it is unknown if the technology will be used for traffic enforcement in Edmonton. As of right now, this research is being used solely to see the prevalence of distracted drivers in Edmonton.

I think [traffic enforcement] is an option that is available to us at [some] point in the future, [however] it is not predominantly the purpose of this study, El-Basyouny said.

In a statement sent out September 13, Jessica Lamarre, director of Safe Mobility for the City of Edmonton, commented on the U of A research project.

This project provides an opportunity to gain a better understanding of the prevalence and safety impacts of distracted driving on our streets through the creative use of new technology alongside our talented research partners at the University of Alberta.

Read the rest here:
Distracted drivers are being identified by artificial intelligence in Edmonton - The Gateway Online

Aspen is known for its world-class skiing, sky-high real-estate prices and breath-taking mountain views. The town has been known to conjure artistic inspiration, as well; its the town where Stevie Nicks reportedly wrote the hit Landslide, and a place John Denver called home for many years.

According to Swift Luxe, there are approximately 1.5 million visitors to Aspen each year who come to take in the beauty of the area.

While its practically impossible to capture Aspen and the surrounding areas beauty in an image, an AI program tried. The images below were created using a program calledDream Studio beta, a more rapid and accessible version ofStable Diffusion, a text-to-image modelthat was released to the public last month.

When this artificial intelligence text-to-image application thinks of Aspen, it thinks of vast mountain ranges.

Aspen, ColoradoStable Diffusion

Aspen, ColoradoStable Diffusion

Aspen, ColoradoStable Diffusion

Aspen, ColoradoStable Diffusion

Aspen, ColoradoStable Diffusion

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This is pretty close if you ask us.

Maroon BellsStable Diffusion

Maroon BellsStable Diffusion

Maroon BellsStable Diffusion

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Aspen Real EstateStable Diffusion

Aspen Real EstateStable Diffusion

Aspen Real EstateStable Diffusion

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Snowmass VillageStable Diffusion

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Close, very close.

Read the rest here:
Artificial intelligence thinks the Aspen area looks like this - The Aspen Times

When I was a kid, my favorite poem was Shel Silversteins The Homework Machine, which summed up my childhood fantasy: a machine that could do my homework at the press of a button. Decades later that technology, the innocuously titled GPT-3, has arrived. It threatens many aspects of university education above all, college writing.

The web-based GPT-3 software program, which was developed by an Elon Musk-backed nonprofit called OpenAI, is a kind of omniscient Siri or Alexa that can turn any prompt into prose. You type in a query say, a list of ingredients (what can I make with eggs, garlic, mushrooms, butter, and feta cheese?) or a genre and prompt (write an inspiring TED Talk on the ways in which authentic leaders can change the world) and GPT-3 spits out a written response. These outputs can be astonishingly specific and tailored. When asked to write a song protesting inhumane treatment of animals in the style of Bob Dylan, the program clearly draws on themes from Dylans Blowin in the Wind:

How many more creatures must suffer?How many more must die?Before we open up our eyesAnd see the harm were causing?

When asked to treat the same issue in the style of Shakespeare, it produces stanzas of iambic tetrameter in appropriately archaic English:

By all the gods that guide this EarthBy all the stars that fill the skyI swear to end this wretched dearthThis blight of blood and butchery.

GPT-3 can write essays, op-eds, Tweets, jokes (admittedly just dad jokes for now), dialogue, advertisements, text messages, and restaurant reviews, to give just a few examples. Each time you click the submit button, the machine learning algorithm pulls from the wisdom of the entire internet and generates a unique output, so that no two end products are the same.

The quality of GPT-3s writing is often striking. I asked the AI to discuss how free speech threatens a dictatorship, by drawing on free speech battles in China and Russia and how these relate to the First Amendment of the U.S. Constitution. The resulting text begins, Free speech is vital to the success of any democracy, but it can also be a thorn in the side of autocrats who seek to control the flow of information and quash dissent. Impressive.

From an essay written by the GPT-3 software program

The current iteration of GPT-3 has its quirks and limitations, to be sure. Most notably, it will write absolutely anything. It will generate a full essay on how George Washington invented the internet or an eerily informed response to 10 steps a serial killer can take to get away with murder. In addition, it stumbles over complex writing tasks. It cannot craft a novel or even a decent short story. Its attempts at scholarly writing I asked it to generate an article on social-role theory and negotiation outcomes are laughable. But how long before the capability is there? Six months ago, GPT-3 struggled with rudimentary queries, and today it can write a reasonable blog post discussing ways an employee can get a promotion from a reluctant boss.

Since the output of every inquiry is original, GPT-3s products cannot be detected by anti-plagiarism software. Anyone can create an account for GPT-3. Each inquiry comes at a cost, but its usually less than a penny and the turnaround is instantaneous. Hiring someone to write a college-level essay, in contrast, currently costs $15 to $35 per page. The near-free price point of GPT-3 is likely to entice many students who would otherwise be priced out of essay-writing services.

It wont be long before GPT-3, and the inevitable copycats, infiltrate the university. The technology is just too good and too cheap not to make its way into the hands of students who would prefer not to spend an evening perfecting the essay I routinely assign on the leadership style of Elon Musk. Ironic that he has bankrolled the technology that makes this evasion possible.

To help me think through what the collision of AI and higher ed might entail, I naturally asked GPT-3 to write an op-ed exploring the ramifications of GPT-3 threatening the integrity of college essays. GPT-3 noted, with mechanical unself-consciousness, that it threatened to undermine the value of a college education. If anyone can produce a high-quality essay using an AI system, it continued, then whats the point of spending four years (and often a lot of money) getting a degree? College degrees would become little more than pieces of paper if they can be easily replicated by machines.

The effects on college students themselves, the algorithm wrote, would be mixed: On the positive side, students would be able to focus on other aspects of their studies and would not have to spend time worrying about writing essays. On the negative side, however, they will not be able to communicate effectively and will have trouble in their future careers. Here GPT-3 may actually be understating the threat to writing: Given the rapid development of AI, what percent of college freshmen today will have jobs that require writing at all by the time they graduate? Some who would once have pursued writing-focused careers will find themselves instead managing the inputs and outputs of AI. And once AI can automate that, even those employees may become redundant. In this new world, the argument for writing as a practical necessity looks decidedly weaker. Even business schools may soon take a liberal-arts approach, framing writing not as career prep but as the foundation of a rich and meaningful life.

So what is a college professor to do? I put the question to GPT-3, which acknowledged that there is no easy answer to this question. Still, I think we can take some sensible measures to reduce the use of GPT-3 or at least push back the clock on its adoption by students. Professors can require students to draw on in-class material in their essays, and to revise their work in response to instructor feedback. We can insist that students cite their sources fully and accurately (something that GPT-3 currently cant do well). We can ask students to produce work in forms that AI cannot (yet) effectively create, such as podcasts, PowerPoints, and verbal presentations. And we can design writing prompts that GPT-3 wont be able to effectively address, such as those that focus on local or university-specific challenges that are not widely discussed online. If necessary, we could even require students to write assignments in an offline, proctored computer lab.

Eventually, we might enter the if you cant beat em, join em phase, in which professors ask students to use AI as a tool and assess their ability to analyze and improve the output. (I am currently experimenting with a minor assignment along these lines.) A recent project on Beethovens 10th symphony suggests how such projects might work. When he died, Beethoven had composed only 5 percent of his 10th symphony. A handful of Beethoven scholars fed the short, completed section into an AI that generated thousands of potential versions of the rest of the symphony. The scholars then sifted through the AI-generated material, identified the best parts, and pieced them together to create a complete symphony. To my somewhat limited ear, it sounds just like Beethoven.

Read more:
Will Artificial Intelligence Kill College Writing? - The Chronicle of Higher Education

SEATTLE, Wash.-

For over two years, protein structure prediction has been changed by machine learning. On Sept. 15, two science related research talk about a similar idea in the revolution of protein design.

The findings show how machine learning can create protein molecules that are more accurate and made quicker than before.

With these new software tools, we should be able to find solutions to long-standing challenges in medicine, energy, and technology, said senior author David Baker, professor of biochemistry at the University of Washington School of Medicine.

The algorithm used in machine learning which includes RoseTTAFold have been trained to predict the smaller detailed shapes if natural proteins based on their amino acid sequences.

Machine learning is a type of artificial intelligence that allows computers to learn from data without having to be programmed.

A.I. has the ability to generate protein in two ways. One being akin to DALL-E or other A.I. tools that produce an output from simple prompts. The second is the autocomplete feature we can find in a search bar.

As a way of making things go by faster the A.I. team created a new algorithm that creates amino acid sequences. This tool, called ProteinMPNN, creates the sequence in one second. That's over 200 minutes faster than previous best software.

The Baker Lab also says combining new machine learning tools could reliably generate new proteins that functioned in the laboratory. Among those were the nanoscale ring that could make up part of a custom nanomachines.

Read the original post:
University of Washington graduates use artificial intelligence to create new proteins - NBC Right Now