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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

Arushi Kapoor

Many people considered it a formless blur of colors, an image that was abstract but slightly resembling a human face. The image isnt even properly positioned on the canvas, rather it is skewed towards the northwest.

In October 2018, this art piece: Portrait of Edmond de Belamy, an algorithm-generated print, was sold for $432,500, thus beginning the AI-Art goldRush.

Humans have always created and enjoyed all forms of art, for viewing purposes, for aesthetic purposes, and even for therapeutic purposes. Since the discoveries of an artistic shell carved by homoerectus, the art business has grown in leaps and bounds and become a highly profitable industry. Leonardo Davincis, Salvator Mundi went for $450.3 million, becoming the most expensive art piece to date.

Understanding and thriving in this industry is not as easy as it may appear, it requires a lot of knowledge, time, and exposure. 25-year-old Arushi Kapoor is the CEO and co-founder of ARTSop art consulting, is an entrepreneur who boasts all of these traits. She is also the founder of Arushi, a cultural center and art warehouse based in Echo Park, Los Angeles.In this article, Kapoor shares her knowledge of the art industry and the influence that tech and AI have on it.

Technology has impacted the way art is created and enjoyed for the better part of the last 100 years, the invention of portable paint tubes enabled artists to paint outdoors and sparked a contingent of stunning landscape and horizon paintings. Today cameras and software like Photoshop have redefined the way art is created and enjoyed.

Kapoor, who is herself a tech-enthusiast agrees that these advancements have been great, but insists that they have not changed the antiquated meaning of art.

I will always be grateful for technology and technological advancements, says Kapoor.I wouldnt have a business or be able to do what I have done in the industry since the age of 19, had it not been for technologies of various kinds.

She continues,However, in my experience, I feel that there is still and will always be that reverence in the hearts of art lovers towards handmade art and crafts. Technological creations have great utility and aesthetic value, but paintings and craft tend to have what I refer to as artistic glory. Human creativity is what art is all about. Technology is a help to it, not a full replacement for it.

Kapoors foray into the industry dates back to when she wrote her first book, Talking Art at age 19. With that book, she put the world on notice that art was not going to be just a fleeting interest for her. Kapoor grew up in India, Europe, and the US, and this multicultural exposure has certainly influenced her knowledge and understanding of art.

Kapoor is the director of Arushi, a US-based venture that made history as the first to present a sold-out all-Indian art show; Art of India, Reclaiming The Present.

ArtSop Consulting, a facet of Arushi, provides private art consulting to people around the world, buying and selling art for clients in the secondary art market. Additionally, ArtSop represents primary artists that are featured in the art warehouse, Arushi.

Kapoor is also a technology investor, who has done a lot of research and invested capital into AI-driven art startups that are moving the needle when it comes to the future of art tech.

Kapoor comments that the integration of AI and art has been received with mixed feelings.

Personally, I havent seen any extraordinary artworks created by AI exclusively yet, she says. I think there is always going to be some human intervention required to create out of the park art. I recently heard, DeviantArt is an AI tool thats helping find stolen artworks. Thats extraordinary and thats how I believe AI can make a positive impact on the art world

The success of the AI-generated Portrait of Edmond de Belamy seems to have sparked off a series of AI art creations all wanting to cash out on the AI intrigue among some high spending art lovers.

In a recent exhibition of prints shown at the HG Contemporary gallery in Chelsea, the epicenter of New Yorks contemporary art world, 20 prints were displayed as part of the Faceless Portraits Transcending Time.

The ARTSop CEO isnt necessarily intrigued by this development, Kapoors MO has always been about highlighting upcoming local and female contemporary artists who have no platform to showcase their creations. In the opening of her Invite-only warehouse in LA, she featured a local female artist, Lindsay Dawn, for her first exhibition. Kapoor believes that real art should be discovered and celebrated.

If AI prints continue to sell for huge amounts it may de-incentivize actual human creation and creativity, says Kapoor.

Arushi Kapoor

At the rate at which technology is being accepted in every industry, it is no longer difficult to imagine a future where fewer artists are creating because they lack platforms to sell. Arushi along with many other art companies and galleries, hopes to find a balance and to create an ecosystem where both kinds of art can co-exist in the future. This shift to accepting non man made artworks isnt widely accepted currently. I am optimistic that there would always be a large section of art lovers who prefer man-made creations or perhaps love both.

Artificial Intelligence wasnt initially applied to art as a creator but as an impersonator. The technique is called style transfer and it uses deep neural networks to replicate, recreate and blend styles of artwork, by teaching the AI to understand existing pieces of art. Alexandra Squire is an excellent example of how the very human process of making art is not easily replicated. Squire believes art is a universal language with vast meanings, and focuses on art that is substantial, open to interpretation, and rich in depth and texture.

The increased usage of all kinds of AI in all kinds of art suggests that it is here to stay. From the AI-written book, 1 The Road, to Anna Riddlers AI-generated blooming tulip videos, creators have found value in utilizing artificial intelligence.

The question then becomes, is AI the future of the art industry? Kapoor shares her sentiment on this pertinent question.

Kapoor adds, The more optimistic view is that artificial intelligence evolves into a greater tool for existing creators to enhance, discover and replicate their works. We all hope for a world where our technologies help us, and not replace us.

Kapoors perspective on the future of art and AI is probably the most tenable and desirable. There is a strong perception amongst art lovers that machines can not produce art in the real sense of the word.

This sentiment is partly true because so far, AI has only demonstrated an ability to study and understand existing art and to somehow enhance or combine them to produce something new, and in some cases, something better.

See the original post here:
Is Artificial Intelligence Set To Take Over The Art Industry? - Forbes

If you want to imagine the future of healthcare, you can do no better than to read cardiologist and bestselling author Eric Topols trilogy on the subject: The Creative Destruction of Medicine, The Patient Will See You Now, and Deep Medicine.

Deep Medicine bears a paradoxical subtitle: How Artificial Intelligence Can Make Healthcare Human Again. The book describes the growing interaction of human and machine brains. Topol envisions a symbiosis, with people and machines working together to assist patients in ways that neither can do alone. In the process, healthcare providers will shed some of the mind-numbing rote tasks they endure today, giving them more time to focus on patients.

I recorded an interview with Topol in which we discuss his books. The podcast is titled Healthcares Reluctant Revolution because one of Topols themes is that healthcare is moving too slowly to integrate AI and machine learning (ML) into medicinea sluggishness that diminishes the quality and quantity of available care.

The first of Topols books, Creative Destruction, described how technology would transform medicine by digitizing data on individual human beings in great detail. In The Patient Will See You Now, he explored how this digital revolution can allow patients to take greater control over their own health and their own care. With this democratization of care, medicines ancient paternalism could fade. (In 2017, Topol and I co-authored an essay on Anatomy and Atrophy of Medical Paternalism.)

Deep Medicine is qualitatively different from the other two books. It has an almost-mystical quality. Intelligent machines engaging in AI and ML arrive at information in ways even their programmers can barely comprehend, if at all. Topol gives a striking example.

Take retinal scans of a large number of peoplethe sort of scans that your optometrist or ophthalmologist takes. Now, show the scans to the top ophthalmologists in the world and ask for each scan, Is this person a man or a woman? The doctors will answer correctly approximately 50 percent of the time. In other words, they have no idea and could do just as well by tossing a coin. Now, run those same scans through a deep neural network (a type of AI/ML system). The machine will answer correctly around 97 percent of the timefor no known reason.

Topol explains how such technologies can improve care. Today, radiologists spend their days intuitively searching for patterns in x-rays, CT scans, and MRIs. In the future, much of the pattern-searching will be automated (and more accurate), and radiologists (who seldom interact with patients today) will have much greater contact with patients.

Today, dermatologists are relatively few in number, so much of the earlier stages of skin care are done by primary care physicians, who have less ability to determine, say, whether a mole is potentially cancerous. The result can be misdiagnosis, delayed diagnosis, and the unnecessary use of dermatologists time. In the future, primary care doctors will likely screen patients using smart diagnostic tools, thereby wasting less of patients and dermatologists time and diagnosing more accurately.

In Deep Medicine, Topol tells the story of a newborn experiencing seizures that could lead to brain damage or death. Routine diagnostics and medications werent helping. Then, a blood sample was sent to a genomics institute that combed through a vast amount of data in a short time and identified a rare genetic disorder thats treatable through dietary restrictions and vitamins. The child went home, seizure-free, in 36 hours.

Unfortunately, healthcares adoption of such technologies is unduly slow. In our conversation, Topol noted that we have around 150 medical schools, some quite new, and yet they dont have any AI or genomics essentially in their curriculum.

Topol lists some hopes that observers invest in AI: Machines outperforming doctors at all tasks, diagnosing the undiagnosable, treating the untreatable, seeing the unseeable on scans, predicting the unpredictable, classifying the unclassifiable, eliminating workflow inefficiencies, eliminating patient harm, curing cancer, and more.

A realistic sort of optimist, Topol writes: Over time, AI will help propel us toward each of these objectives, but its going to be a marathon without a finish line.

Read the rest here:
Artificial Intelligence and the Humanization of Medicine InsideSources - InsideSources

Distribution centers provide a controlled environment that is ideal for testing and proving complex technologies like drones and robots. That's also one reason why DCs are experimenting heavily with Artificial Intelligence (AI).

An independent research survey commissioned by Lucas Systems found that the majority of companies are already using AI in their warehouses and distribution/fulfillment operations. The survey also revealed that operators view cost, complexity, and lack of understanding of how to use AI as key impediments to further investments.

In reality, AI will make it easier and less costly for DCs of all sizes to address warehouse optimization challenges like slotting and workforce planning. And successful use of AI will not require massive investments in data science departments. Here's why.

Good data is a key to effective AI, and DCs are a good environment for collecting and aggregating historical and real-time data. AI is also a natural fit for DC operational challenges that previously required highly-engineered expert systems that are costly to implement and maintain.

AI and machine learning-based solutions reduce those obstacles, and they give DCs better results than current resource and inventory management approaches that rely on Excel, inherited best practices, or simple rules-based decision-making. AI is making advanced optimization practical for smaller operations, and more flexible and cost-effective for larger facilities.

Lucas has identified five key applications for AI in the warehouse today.

Proper product slotting impacts labor productivity, throughput, and accuracy, but doing it well isn't easy. Slotting is both a combinatorial optimization problem (many input factors to consider) and a multiple objective optimization problem (with many goals, sometimes competing). In addition, there are thousands of products and product locations (slots) to consider, and those products and locations may change frequently.Traditional slotting solutions require customized models and extensive engineering, measurement and data collection, both to install and maintain.

AI eliminates much of the engineering work and manual warehouse mapping and data inputs required for traditional slotting systems. AI-based software can learn the spatial characteristics and travel time predictions required for a slotting model based on activity-level data captured in the DC. And the learned model will adapt as conditions change, providing continuous optimization.

Optimal labor allocation is essential to ensuring orders get out on time while eliminating overstaffing and understaffing. In many DCs, supervisors make staff allocation decisions throughout a shift based on the volume of work, deadlines, and current and expected productivity. Good decisions require good data and accurate predictions, which today are often based on each manager's individual experience and skill.

To improve results, machine learning can be applied to predict labor requirements and work completion times. An AI solution can also run simulations to determine how to best complete the work, avoiding delays and ensuring the most efficient use of labor.

Labor management systems using Engineered Labor Standards (ELS) have been around for years. AI can eliminate much of the labor-intensive data collection process required with ELS-based performance management, using learning algorithms to predict the time required to complete tasks.

AI algorithms learn based on real-world performance data collected from within the operation, taking into account a multitude of variables (user, work type, work area, starting travel location, ending travel location, product to be handled, quantity to be handled, etc.).The predicted results and expectations are more accurate and the ML models adjust when operational changes are introduced.

Warehouse workers spend much of their workday traveling within a facility, making travel reduction a key to improved productivity. Automation and robots each eliminate travel, and AI can be used in areas where automation alone is not enough.

AI and machine learning systems use large amounts of process data to 'learn'how to balance priorities and reduce travel through intelligent order batching and pick sequencing. The systems take into account common congestion areas and slow-moving routes. Many DCs have achieved 2x productivity gains in piece picking applications using AI-based travel reduction, and even case pick to pallet operations have demonstrated 20-30 percent productivity gains.

The same tools used to optimize travel for workers can apply to orchestrating people and autonomous mobile robots (AMRs) in an order-picking process. In most pick-to-robot systems today, the robot system optimizes and directs the robots to a location, and a nearby worker delivers one or more picks to the robot based on instructions on a tablet mounted to the machine.

An AI-based execution system can orchestrate and optimize for both the robots'and the pickers'time while also providing means to direct workers independent of the AMRs (using wearable mobile devices rather than robot-mounted tablets).Machine learning algorithms predict where the robots and pickers will be located at a given time, and other algorithms provide input to intelligently organize and sequence the work among people and robots.

In the survey mentioned earlier, the cost was seen as the biggest impediment to AI adoption, and 8 in 10 of the respondents also said their organizations need a better understanding of how AI can be used in the DC.

As outlined above, AI has the potential to reduce the cost and manual engineering time and effort required to implement a range of DC optimization solutions, from slotting to labor performance management. What's more, these new AI-based solutions do not require that companies develop extensive in-house AI expertise.

Read the original:
5 applications for artificial intelligence in the warehouse and distribution center - Supply Chain Dive

Introduction by Croakey: As with the COVID-19 pandemic, health authorities usually identify epidemics through public health surveillance, but could we do it earlier by being able to mine the vast un-curated public data available to us in this digital age?

Thats the hope and challenge from leading epidemiologist, Professor Raina MacIntyre, who heads the Biosecurity Program at the Kirby Institute, and Arunn Jegan, Advocacy Coordinator at Mdecins Sans Frontires (MSF).

They write below on the potential for harnessing artificial intelligence and the proliferation of the internet and social media for early detection of epidemics, saying that a signal for unusual pneumonia in China could have been detected in November 2019 and that CSIRO research showed that the Ebola epidemic in West Africa could have been detected three months before the World Health Organization was aware of it.

They ask:

Imagine if the COVID-19 pandemic had been detected well before it spread around the world, when there was only a handful of cases contained within a small geographic location?

Readers may also be interested in the series of articles published by the Croakey Conference News Service from the recent World Congress of Epidemiology, with one on innovative Victorian disease surveillance measures and their critical role in the states COVID response to be published soon.

The SARS-COV2 (COVID-19) pandemic has caused devastation around the world, and even in vaccinated populations, it continues to mutate into dangerous variants of concern. With the onset of the Delta variant, we assume the death toll will rise beyond five million in 2021.

This is an epidemic disease, which means it grows exponentially. One case today will be five cases in a few days and then 25 cases and so on. So, time is of the essence, and the sooner you can identify epidemics, the better the prospect of stamping it out and preventing global spread.

Imagine if the COVID-19 pandemic had been detected well before it spread around the world, when there was only a handful of cases contained within a small geographic location?

Isolating cases and tracing and quarantining their contacts may have been enough to stop it spreading.

Exponential growth and time are the enemies we face with epidemic diseases the longer we take to act, the larger the epidemic will become and over a very short period. Just look at the Sydney outbreak which started in Bondi in June 2021.

Recall the West African Ebola epidemic in 2014. It was 67 times the size of the largest previously recorded Ebola outbreak, it reached urban areas, and killed more than 11,300 people.

Ebola outbreaks can kill 25 to 90 percent of those infected. In 2014-15, hundreds of health workers died, decimating the already-struggling healthcare systems of Liberia, Guinea, and Sierra Leone. Medecins Sans Frontieres (MSF) responded in each of these contexts.

In the Ebola outbreak, with fears of a pandemic on the horizon, organisations like the World Health Organization (WHO), MSF, and others supported national health systems by treating and isolating patients; tracing and follow up of patient contacts; raising community awareness of the disease such as how to prevent it and where to seek care; conducting safe burials; proactively detecting new cases; and supporting existing health structures.

When WHO was first notified of Ebola in March 2014, it may have comprised a few 100 cases, but it grew exponentially. By August 2014, the case numbers were in the thousands, and by October over 20,000 cases had occurred.

Furthermore, until only very recently, there were no tools to prevent or treat Ebola. Today a preventive vaccine and curative drugs are available. Imagine how many lives could have been saved if the epidemic had been detected when there were only a handful of cases.

Prior to COVID-19 in 2019, the Ebola epidemic saw the fastest trajectory to development of a vaccine, with Phase 1 trials in Oct 2014 to the approval of this vaccine in Nov 2019. Indeed, the average time was 10-15 years prior to both COVID-19 and Ebola vaccine developments.

For COVID-19, vaccines were developed and ready for use in less than 12 months, but after devastating global consequences of the pandemic and the hundreds of thousands killed in the global north.

In short, it is unwise to rely solely upon vaccines and or/ their development to manage an epidemic, especially in low-resource settings. Non-pharmaceutical interventions such as testing, tracing and measures to reduce contact between people are also important.

We have had a measles vaccine since the 1960s, however the disease rages through the world in epidemic proportions in over 41 countries such the Democratic Republic of Congo and Central African Republic.

The primary reason behind this is a deeply inequitable, and unfair global biomedical system which has unfairly provided for wealthier countries but not low-income countries.

We are seeing it play out with COVID-19, where only 1.8 per cent of people in low-income countries have received one dose, out of 5.4 billion doses administered globally.

With COVID-19, the general public have had a taste of what epidemiologists have known for decades, that strong health surveillance is essential to getting on top of outbreaks and to have any chance of zero elimination strategies, or any suppression strategy for that matter, working.

While the global disparity of vaccination rates persists, what new technologies is Australia investing in to helping communities get on-top of outbreaks and bolster health surveillance?

How are we harnessing artificial intelligence together with the proliferation of the internet and social media for early detection of epidemics?

The usual way we identify epidemics is through public health surveillance which is when labs or doctors notify health authorities of unusual, serious, or notifiable infections.

When lots of these notifications start piling up, or a trend is seen of higher case numbers than usual, the health official may investigate a possible outbreak.

But people talk about illness in their communities, and local news agencies report on unusual outbreaks, long before health officials know about it.

What if we could mine the vast, un-curated public data available to us in this digital age and detect signals of epidemics early?

At UNSW, the EpiWatch observatory does just that, tapping into news reports from around the world, in many different languages, using algorithms and artificial intelligence (AI) to detect early outbreak signals.

We showed that a signal for unusual pneumonia in China could have been detected in November 2019; and CSIRO research showed that the Ebola epidemic in West Africa could have been detected three months before WHO was aware of it.

This is in no way a replacement for in-country based data collection or existing Early Warning, and Alert Response Systems (EWARS). Using AI in epidemiology is an additional tool that uses innovative technologies and has the potential to reach communities who do not have the strongest national health surveillance systems.

It can also overcome censorship of information to detect signals in countries that are withholding outbreak information from the world. Reasons for censorship include fear of impacts on tourism, trade, or other parts of the economy, or political reasons.

Traditionally, declaring epidemics rest solely on the responsibility of governments, but never in human history has there been more attention on virology and epidemiology from the public. Therefore, ensuring that data-collected from the internet follows scientific modelling and surveying has never been more important.

As with most emergent technology using data and information to inform a product, the ethics over use of open-source data and safe-guards will need to be in place on who this empowers. Generally, however, methods such as used by Epiwatch do not utilise identifying or private information.

Moving forward the Kirby Institute at UNSW, with CSIRO Data 61 is exploring with MSF on how best AI can be utilised to detect epidemics as fast as possible and give vulnerable communities in low-income countries a fighting chance when epidemics strike.

Applying our lessons learnt from the COVID-19 pandemic and Ebola, now is the right time for Australia and the humanitarian community to invest in innovative health surveillance systems, and to keep potential epidemics isolated to save lives.

Professor Raina MacIntyre is NHMRC Principal Research Fellow and Professor of Global Biosecurity. She heads the Biosecurity Program at the Kirby Institute, which conducts research in epidemiology, vaccinology, bioterrorism prevention, mathematical modelling, genetic epidemiology, public health and clinical trials in infectious diseases.

Arunn Jegan is Advocacy Coordinator at Mdecins Sans Frontires (MSF) Australia. He is also the Permanent Facilitator for the emergency public health course at Epicentre in Paris. Arunn has worked as Head of Mission and Emergency Coordinator and has worked in Yemen, Syria, Venezuela, Bangladesh for MSF and in Afghanistan, Iraq, Jordan, Lebanon, and Turkey in senior management positions for other international NGOs. He specialises in social research, conflict/political analysis, complex project management, and humanitarian crisis coordination of public health emergencies.

See the Croakey Conference News Service coverage from the World Congress of Epidemiology.

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Harnessing artificial intelligence to help prevent epidemics before they spread - Croakey Health Media