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Second AI Awards See A 50% Increase in Entries from A Diverse Range of Organisations – socPub

Posted on October 1, 2019 by Danzig

Accenture, AIB, ESB, National Transport Authority, INFANT Centre amongst those recognised for innovation in Artificial Intelligence.

Entries from Trinity College, UCC, Waterford Institute of Technology and DCU reflect increase in academic research in AI.

Nominations demonstrate innovation across health, customer service, and science.

Dublin October 1st, 2019: Today, AI Ireland received a record 50% increase in entries in its second year, showcasing innovation in Artificial Intelligence across healthcare, finance, customer service, communications, and academia.

The second AI Awards 2019 will be presented at the Gibson Hotel on Wednesday 20th November. These Awards, which are part of the not-for-profit organisation AI Ireland, support the growth and development of Data Science, Machine learning, and Artificial Intelligence in Ireland.

This year also saw the introduction of a new category for Intelligent Automation-Best Use of RPA (Robotic Process Automation) to reflect the rapidly growing sector within AI in the Irish market.

The increase in nominations shows the Irish AI sector is innovating with potentially game changing outcomes for organisations and society, said Mark Kelly, Chief Customer Officer at Alldus & Founder of AI Ireland.

The sheer breadth of AI and Machine Learning, research, development and implementation across private, public and academic organisations is exciting and shows how we are embracing the potentially massive opportunities and benefits that AI can bring.

Cathriona Hallahan, Managing Director, Microsoft Ireland, At Microsoft, we are infusing AI into everything we deliver, across our computing platforms and experiences, as we believe democratising access to intelligence will help solve the worlds most pressing challenges. We are committed to driving AI adoption and innovation in Ireland which will support the ambition of Ireland being a digital leader in Europe. The AI awards are key to fostering and recognising homegrown talent and entrepreneurship and we are delighted to support the awards for a second year and to see to the quality of the applications increase. We look forward to the exciting innovations that will be showcased at the ceremony in November in the hope they will enable more people and organisations to do more into the future.

The 2019 AI Awards Shortlists.

Best Application of AI in a Large Enterprise

Accenture for their Job Matching solution.

Allied Irish Bank (AIB) for their AIB Services Insights Project.

Johnson Controls for their first-of-its-kind AI-powered security product, known as Converged Cyber-Physical Security (CCS).

Mastercard Labs for Duka Connect, a Mobile Point of Sale (mPoS) solution for small merchants in emerging markets.

Best Use of AI in a Consumer/Customer Service Application

Accenture for their Knowledge Exchange (KX) AI integration.

Idiro Analytics for their work with Digicel in integrating AI into customer services to reduce churn.

SAP for their Business Operations and Self-Healing (BoSh), an out of the box AI platform to support business automation.

Webio for their Conversational Middleware Service enabling organisations to connect applications, digital assets across different communications platforms (SMS, WhatsApp to Alexa).

Best Application of AI in a Student Project

Meredith Telford, Ulster University for her work on using AI to accelerate production of 3D printed cardiac models using machine learning, improving diagnosis and enabling surgeons to practice virtually before surgery.

Cian Vaughan, National College of Ireland for his work integrating movement and gesture recognition for Irish Sign Language to help deaf people fluently interact with devices in the future.

Ciaran O'Mara, University of Limerick for his work on Machine Learning Based Traffic Network Analysis Tool, using AI for traffic management.

Rory Boyle, Trinity College Dublin for his work on brain predicted age difference score (brainPAD), a way of representing brain health outside of a patients chronological age.

Best Use of AI in Sector

Liopa for their LipRead technology that uses AI in Visual Speech Recognition (VSR) or automated lip reading.

National Transport Authority - for their use of AI to better analyse data to provide high-quality accessible and sustainable transport solutions nationwide.

Soapbox Labs for their work on developing speech recognition technology for children.

TVadSync for its Smart TV based Automatic Content Recognition (ACR) that provides brands and marketers insight into the ad effectiveness of their media campaigns as well as deep behavioural analysis of their customer base.

NEW AWARD! Intelligent Automation - Best Use of RPA & Cognitive

Doosan Bobcat for their work it UiPath to use Robotic Process Automation to automate tasks for employees, delivering savings of 400 hours per month.

ESB for using AI to optimise its rollout of smart meter technology.

HealthBeacon for their digitally connected Smart Sharp Bin service that supports patients who self-inject medications at home to ensure adherence to medical treatment schedule.

McKesson for their work on integrating AI tools like RPA to standardise approach to incorporate the various sources of data and systems that combine to allow the day-to- day activity to occur across Europe.

Best Application of AI in an Academic Research Body

Connect Centre, Dublin City University for their work using AI to optimise superfast internet speeds by tackling non-linear distortions in optical fibre networks.

Connect Centre, Waterford Institute of Technology for their work on SmartHerd, an IOT based system to predict lameness in dairy cattle.

INFANT Centre, University College Cork for their work using deep learning to detect neonatal seizure detection.

Sigmedia, Trinity College Dublin for their work in combining visual and speech cues in a speech recognition system.

Best Application of AI in a Startup

Getvisibility for their use of Machine Learning and Natural Language Processing to discover and categorise unstructured data sets.

Rinocloud for their work in using AI to reduce time and risk in research and development and enhancement of skin disease remedies affecting 3% of the global population and 20% of children under 10 years of age.

Telenostic for their work on deep learning or convolution neural networks (CNNs) to accurately model and predict Parasitic Infections (PI) within animals.

Truata for their work in providing a new standard in data hosting and anonymisation. Using proprietary processes, methodologies and intellectual property, the solution makes it possible for organizations to analyse their data while complying with privacy and data protection regulations.

Microsoft Ireland is the principal sponsor of the 2019 AI Awards. The awards will also be further supported by the IDA Ireland, Alldus, ISG, McKesson, Mazars, Mason Hayes Curran, the ADAPT Centre and GeoDirectory.

For more information, visit http://www.aiawards.ie

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Second AI Awards See A 50% Increase in Entries from A Diverse Range of Organisations - socPub

AI Weekly: CDPA bill shows progress on coronavirus-tracking data privacy, but theres still a ways to go – VentureBeat

Posted on May 9, 2020 by Danzig

Contact tracing has quickly emerged as the go-to method of tracking the spread of the coronavirus among the general population, but there have been crucial questions around the most effective, ethical, and legal ways of doing so. New legislation introduced this week, the COVID-19 Consumer Data Protection Act (CDPA), seeks to enact legal guardrails around the collection and use of peoples data.

Its a sign of progress that legislation is emerging around this issue, but it also highlights that theres a way to go yet. The CDPA has some issues that privacy experts are concerned about, and the lack of any Democrat co-sponsors indicates a lack of bipartisan support. The Dems actually have their own version of this type of legislation, called the Consumer Online Privacy Rights Act (COPRA), which was introduced in December. Both bills emerged from the same committee the Senate Committee on Commerce, Science, and Transportation and so the lack of bipartisanship is especially notable.

The CDPA was introduced by Senators Roger Wicker (R-MS), John Thune (R-SD), Deb Fischer (R-NE), Jerry Moran (R-S), and Marsha Blackburn (R-TN). COPRA is sponsored by Senators Maria Cantwell (D-WA), along with Brian Schatz (D-HI), Amy Klobuchar (D-MN), and Ed Markey (D-MA).

Despite the partisanship, the CDPA includes much that all sides can agree on. And in an announcement about the bill, the Republican Senators said all the right things. For example, Senator Wickers statement reads, As the coronavirus continues to take a heavy toll on our economy and American life, government officials and health-care professionals have rightly turned to data to help fight this global pandemic. This data has great potential to help us contain the virus and limit future outbreaks, but we need to ensure that individuals personal information is safe from misuse.

Per the announcement, the CDPA includes the following:

In a statement to VentureBeat, Liz OSullivan, cofounder of ArthurAI and technology director of STOP (Surveillance Technology Oversight Project), said that the CDPA is a step in the right direction, but shes concerned that it doesnt go far enough. Theres nothing stopping companies from using this data to profit after the crisis, and it wont protect people in the event that ICE or other law enforcement agencies subpoena identifiable information while the crisis is ongoing, she said.

In a way, the issues here are business as usual for data privacy. All the usual concerns apply: This data is a great source of power in any hands, to be politicized or used for personal gain. If companies are left with a choice to delete or de-identify, its pretty clear which one they will choose, she said, adding that Its telling, in fact, that Palantir, a company typically associated with national security, has already won contracts to handle this data.

She emphasized that the danger with any bill that fails to keep a divide between public and private data is the creation of the illusion of privacy while handing governments, and state-adjacent corporate entities, expanded surveillance capabilities.

Andrew Burt, chief legal officer at Immuta and managing partner at bnh.ai, said in a statement to VentureBeat that the CDPA does serve to reinforce how important data and data analytics are to combatting the pandemic. Theres a reason, for example, that the most thorough plans to get Americans back to work pre-vaccine start with contact tracing and monitoring knowing who might be a carrier of the virus, and where theyve gone and who theyve been in close proximity to, is the first step to getting us to a state of reasonable safety, he said. Data collection and data analytics will form the backbone of those efforts. So I see the CDPA as a very clear acknowledgement of that fact.

But Burt also noted that there is much more that needs to be discussed around data protection laws, such as what a bill like this says about the broader state of data protection laws, the current and future role of the FTC around privacy, what counts as health data in a world of ubiquitous data generation and collection, applying time limits to new surveillance mechanisms for COVID-19, and more.

The fact that legislators are moving forward with data privacy laws is a welcome sign of progress. But Republicans and Democrats will need to do more to come to consensus lest the U.S. ends up with data laws that fail to strike the best balance between protecting people from the coronavirus and protecting people from future abuses.

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AI Weekly: CDPA bill shows progress on coronavirus-tracking data privacy, but theres still a ways to go - VentureBeat

AI Adoption Hitting Barriers? Here’s How to Get to the Next Level – IndustryWeek

Posted on July 29, 2020 by Danzig

When manufacturers strategically deploy artificial intelligence (AI) into their operating environments, the results can be game changing. Setting the stage for constant innovation. Avoiding down production lines. Anticipating and taking action on dramatic market changes before the competition. The reality for most manufacturers? AI deployments can be challenging. Getting it right takes determining and dedication to optimizing the process.

I recently connected with Landing AI's Quinn Killough to discuss what happens, and how manufacturers can move past the obstacles.

IW: When it comes to adopting AI, where do most manufacturers run into problems?

Killough: One of the major points of failure in AI projects for manufacturers is the jump from a working model in the lab to a working model in production. Many manufacturers have high hopes for AI improving automation on their plant floors, and once theyve honed-in on a valuable problem/project, they go to work in the lab and start training AI models. After just a few iterations (depending on the complexity of the problem) they may find their model is performing at the accuracy level desired.

They think, great! AI is magic and its going to solve all of our problems, and decide to take the solution to production so they can start reaping the benefits. The issue that we have seen with countless manufacturers across a number of industries is that the models performance in production is worse than it was in the lab and is not yet suitable for true deployment.

They end up needing to go back to the lab and the drawing board to iterate on the model further and in the meantime revert back to their old way of doing things on the floor. As we see it, this jump from a PoC in the lab to a fully functional, value adding, deployed model is much harder than expected. Not only is this an issue for the project, i.e. longer development times, more money spent, opportunity cost of the old system running longer, but it is also an issue of perception for AI - people can lose faith after seeing this process unfold.Landing AI's Quinn Killough

IW: Why do manufacturers struggle to get past that point?

Killough: Frequently, the number one contributor to this struggle is data quality. A machine learning model is only as good as the data you are putting into it. Machine learning engineers spend up to 80% of their time prepping their data, and there is a lot that can go wrong in this phase. Issues with data quality can come in many flavors, but the two we see the most often are poorly labeled data, and bad dataset distribution.

A model will struggle in production if the manufacturer fails to accurately label the data. When you dont have millions of datasets to wash out the impact of labeling mistakes, just one or two improperly labeled items can lead to a model that does not perform well. In the case of visual defect detection in manufacturing, a common contributor to bad labels is inadequate defect definitions. It is not uncommon for two subject matter experts on a plant floor to disagree on whether a defect is a scratch or a crack, which in turn makes it difficult for the people doing labeling to label images correctly.

When machine learning engineers are collecting data to train their model, the data collected often is not diverse enough to represent the variety of edge cases that would actually be seen in the production environment. Because of this, edge cases that are not seen frequently may be underrepresented when training and testing so the model ends up not being great at finding these. Lets consider a visual inspection project where a manufacturer is looking for several categories of defects on a phone screen - dust, dead pixels and cracks. Dust and dead pixels are super common, so they have a ton of data on these categories. Cracks, on the other hand, are a very infrequent defect and therefore there is less data and the model is not great at recognizing them. A big issue here is that oftentimes the infrequent defects are some of the most critical ones to catch. If this manufacturer were to test on the same distribution as they trained they would get good results. But when going to production they would quickly find out they cant perform on their most critical defects and be forced to pull the system.

IW: What are the keys to getting past the barriers?

Killough: There are a couple of things that can be done to help get past these barriers and achieve a successful deployed model.

First, create an airtight labeling process, and second, move the project out of the lab sooner. This starts with data collection and making sure your dataset is as representative of the production process as possible. After a well-balanced dataset is established, a systematic method for driving agreement on what the definitions of the label categories are needs to be incorporated. Consensus tasks have proven to be a great way to achieve this - i.e. create a system where multiple experts review that same data and compare labels, and after this, discrepancies can be resolved in order to make label definitions less ambiguous for labelers. Now that labelers have the clearest instructions possible, they have a good chance at labeling the data accurately, but they are still human and will likely make mistakes - this is why we would recommend having a tight review process. This means that every single piece of labeled data is reviewed. It may seem like overkill, but a single mistake can be costly on model performance. Establishing this airtight data preparation process will inherently improve model performance and help project leaders avoid costly pitfalls and mistakes.

Outside of having an airtight data preparation process, we also recommend utilizing approaches that allow you to get from the lab bench to the production floor faster than most would think is intuitive. Often teams want to achieve their ultimate goal for model performance - lets say 95% accuracy is the goal - in the lab before moving out to production, but this can be flawed. There will inevitably be unforeseen changes as well as the edge cases mentioned earlier that pop up when deployed to production that hurts performance. Instead, if we spend less time in the lab to get to a lower bar of 80%, we can then deploy that model in production and start uncovering those production edge cases and issues much sooner. To do this, AI teams can use two approaches - shadow mode or putting a human in the loop. Shadow mode means running a model on actual production data without the predictions of the models impacting production - meaning your existing process is not impacted while at the same time you can iterate and improve your model.

Putting a human in the loop is another option if youd still like to benefit from an underperforming model while it is being improved. In this situation the model would run as though it was actually deployed, and a human would review each low confidence prediction. Now a low performing model can be deployed earlier, 100% of parts can still be inspected, and the model can be iterated upon to continuously do more and more of the work until fully autonomous.

IW: Once manufacturers are able to adopt AI, what steps can they take to optimize its use?

Killough: Continuous learning is the most important aspect to optimizing a deployed solution. This entails a few different aspects including data collection, inference monitoring, and a method for retraining.

Model deployment is just the beginning. It takes a solid system just to maintain current performance of a model, let alone further performance optimization. The first step here is having a method for continuous data collection. As the model is making predictions its important to be able to collect data where it may have struggled. With an inference monitoring system where operators can view real time and past predictions this process can be fairly simple.

While collecting and labeling data, a periodic retraining of the model is also very important. This periodic retraining on new data helps to optimize performance by catching and training on those infrequent edge cases. Also, by continuously retraining on the latest state of the production system you can account for naturally occurring changes in production. For example, in optical inspections, if a material changes in your production it could impact model performance due to a new appearance of the product, but not enough to set off any alarms or flag anyones attention. Having a quick and easy way to retrain and roll out a new model is important when trying to optimize or maintain an AI system - without the help of automation this task can be rather time consuming.

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AI Adoption Hitting Barriers? Here's How to Get to the Next Level - IndustryWeek

BSC Working Towards Adoption of AI Applications to Capture Insights on Personalised Healthcare – HPCwire

Posted on February 4, 2021 by Danzig

Feb. 4, 2021 BSC participates in the AI-SPRINTproject contributing its experience on the programming and parallelization of applications on distributed infrastructures. The work will be organized in two main contributions, the deployment ofCOMPSson the edge devices considered by the use cases and the implementation of the AI applications to be executed across distributed heterogeneous infrastructures (on premise, edge and public clouds). In particular,AI-SPRINTwill benefit from the recent developments on the adaptation ofCOMPSsfor Fog to Cloud platforms and will extend it to support the execution of serverless functions as a service.

On the other side,COMPSswill be adopted to develop AI and big data applications in support of the use cases also leveraging the recent enhancements to develop workflows that combine HPC compute engines with High Performance Data Analytics (HPDA) and machine learning methods. These ML implementations are available through thedislib librarythat is also part of theFujitsu-BSCcollaboration.

The technology developed within the project will be put to test by BSC on a personalized healthcare use case that will focus on privacy and security, much needed in healthcare scenarios since the information to be exchanged and processed involves medical data about patients.

More specifically, an automated system for personalized stroke risk assessment and prevention will be developed by using continuous, non-invasive monitoring of heart activity. The process will gather heart parameters collected from a wearable device, patients lifestyle information and biochemical blood indicators from a mobile application. All data will be anonymized, processed and used to train AI models cooperatively by local edge servers and cloud. At the same time, it will provide personalized notifications, alerts, and recommendations for stroke prevention.

AI-SPRINTdefines a novel framework for the design and operation of AI applications in computing continua leveraging theCOMPSsprogramming framework and supporting AI applications development by enabling the seamless design and partition of AI applications among the plethora of cloud-based solutions and AI-based sensor devices. Moreover it will generate impacts bringing together different European industrial end-users while and making available the software tools through a marketplace for AI start-ups, SMEs, system integrators, and European cloud providers statesDaniele Lezzi, Senior Researcher in theComputer Sciences department Workflows and Distributed Computingat BSC.

About COMPSs:

COMPSsis a task-based programming model known for notably improving the performance of large-scale applications by automatically parallelizing their execution. TheCOMPSsruntime has been recently extended within BSC projects:CLASSandELASTICto manage distribution, parallelism and heterogeneity in the edge resources transparently to the application programmer and to handle data regardless of persistency by supporting a single and unified data model.COMPSsis the base of the Design Tools of the project and it will support developers to easily compose AI/ML applications also leveraging the dislib library, helping end users to deal with big datasets on distributed resources and providing automatic parallelization of the code.

About Personalized Healthcare:

AI-SPRINTapplications will pave the way for an effective framework for personalized AI models preventing risks coupled with a lifestyle modificationmodification programmebenefiting people aged between 40 and 80, improving and extending human lives. The project addresses theUnited Nations strategic development goalsSDG3 (Good Health and Well Being) through the personalized healthcare pilot.

About AI-SPRINT

AI-SPRINTwill tackle the skill shortage and considerably reduce steep learning curves in the development of AI software on edge ecosystems through OSS (Operations Support System). The project addresses the followingUnited Nations strategic development goals(SDGs): SDG8 (Decent Work and Economic Growth) enabling novel AI applications running in computing continua, SDG9 (Industry, Innovation and Infrastructure) by fostering innovation in the maintenance and inspection use case and contributing OSS and SDG12 (Ensure sustainable consumption and production patterns) through farming 4.0 pilot.

For further information, visit AI-SPRINT website:https://www.ai-sprint-project.eu/

The AI-SPRINT project has received funding from the European Union Horizon 2020 research and innovation programme under Grant Agreement No. 101016577

Source: BSC

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BSC Working Towards Adoption of AI Applications to Capture Insights on Personalised Healthcare - HPCwire

Latest experiments reveal AI is still terrible at naming paint colors – Ars Technica

Posted on July 10, 2017 by Danzig

Several weeks ago, artist and coder Janelle Shane tried to train a neural network to name paint colors. The results were...not good. "Stanky Bean" was a kind of dull pink, and "Stoner Blue" was gray. Then there were the three shades of brown known as "Dope," "Burble Simp," and "Turdly."

First, Shane realized that part of the initial problem was that she'd cranked up the neural net's "temperature" variable, which meant that it was picking less likely (or "more creative") possibilities as it generated paint names letter-by-letter. So she turned the temperature variable down, and found that the names were still pretty silly but they at least matched the colors most of the time. Plus, the colors themselves seemed more varied:

Next, Shane incorporated some suggestions from experts about how to tweak her dataset. Mostly, what people wanted to know was whether she'd get better results if she changed the way she represented colors. For her original dataset, she used RGB colors, which assign a numeric value to a number based on its blend of red, green, and blue. The AI would randomly generate a number within the RGB space, and slap a name on it.

The problem is, according to Shane, that RGB doesn't do a good job representing color the way human eyes perceive it. Plus, the AI kept coming up with muddy, boring colors that seemed like variations on gray and brown.

She tried two other color schemes: HSV and LAB. On her blog, Shane writes:

In HSV representation, a single color is represented by three numbers like in RGB, but this time they stand for Hue, Saturation, and Value. You can think of the Hue number as representing the color, Saturation as representing how intense (vs gray) the color is, and Value as representing the brightness. Other than the way of representing the color, everything else about the dataset and the neural network are the same...In [the LAB] color space, the first number stands for lightness, the second number stands for the amount of green vs red, and the third number stands for the the amount of blue vs. yellow.

Sadly, HSV and LAB colors didn't really produce results that made more sense than the RGB ones. Shane wound up deciding that RBG was her best option. "Maybe its more resistant to disruption when the temperature setting introduces randomness," Shane mused. The color names, however, were still "pretty bad."

But then one person sent Shane this cleaned-up dataset, which had no capital letters (so there wouldn't be two versions of each letter). It combined paint names from Behr and Benjamin Moore, as well as ones from a user-generated list created by readers of XKCD. Shane called the results "surprisingly good," and noted that the lesson here is that better data generally produces better results. And here they are:

Shane also included a "hall of fame" from her experiments with all the color schemes, which truly represent the promise of machine learning in our modern world:

Listing image by Janelle Shane

AI is coming to war, regardless of Elon Musk’s well-meaning concern – The Independent

Posted on August 24, 2017 by Danzig

A child reacts after a big wave on a waterfront as Typhoon Hato hits Hong Kong

Reuters/Tyrone Siu

Soldiers march during a changing of the Guard at the Mamayev Kurgan World War Two memorial complex and Mother Homeland statue (back) in Volgograd, Russia

Mladen Antonov/AFP

Italian emergency workers rescue a baby (C) after an earthquake hit the popular Italian tourist island of Ischia, off the coast of Naples, causing several buildings to collapse overnight. A magnitude-4.0 earthquake struck the Italian holiday island of Ischia, causing destruction that left two people dead at peak tourist season, authorities said, as rescue workers struggled to free two children from the rubble

AFP/Mauro Pagnano

Damage to the portside is visible as the Guided-missile destroyer USS John S. McCain (DDG 56) steers towards Changi naval base in Singapore following a collision with the merchant vessel Alnic MC. The USS John S. McCain was docked at Singapore's naval base with "significant damage" to its hull after an early morning collision with the Alnic MC as vessels from several nations searched Monday for missing U.S. sailors.

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A protester covers her eyes with a China flag to imply Goddess of Justice during the rally supporting young activists Joshua Wong, Nathan Law and Alex Chow in central in Hong Kong, Hong Kong. Pro-democracy activists Joshua Wong, Nathan Law and Alex Chow were jailed last week after being convicted of unlawful assembly.

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An extreme cycling enthusiast performs a stunt with a bicycle before falling into the East Lake in Wuhan, Hubei province, China. This activity, which requires participants to ride their bikes and jump into the lake, attracts many extreme cycling enthusiasts from the city.

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People gather around tributes laid on Las Ramblas near to the scene of yesterday's terrorist attack in Barcelona, Spain. Fourteen people were killed and dozens injured when a van hit crowds in the Las Ramblas area of Barcelona on Thursday. Spanish police have also killed five suspected terrorists in the town of Cambrils to stop a second terrorist attack.

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Participants take part in Panjat Pinang, a pole climbing contest, as part of festivities marking Indonesia's 72nd Independence Day on Ancol beach in Jakarta. Panjat Pinang, a tradition dating back to the Dutch colonial days, is one of the most popular traditions for celebrating Indonesia's Independence Day.

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Demonstrators participate in a march and rally against white supremacy in downtown Philadelphia, Pennsylvania. Demonstrations are being held following clashes between white supremacists and counter-protestors in Charlottesville, Virginia over the weekend. Heather Heyer, 32, was killed in Charlottesville when a car allegedly driven by James Alex Fields Jr. barreled into a crowd of counter-protesters following violence at the Unite the Right rally.

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South Korea protesters hold placards with an illustration of U.S. President Donald Trump during a during a 72nd Liberation Day rally in Seoul, South Korea. Korea was liberated from Japan's 35-year colonial rule on August 15, 1945 at the end of World War II.

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The Chattrapathi Shivaji Terminus railway station is lit in the colours of India's flag ahead of the country's Independence Day in Mumbai. Indian Independence Day is celebrated annually on 15 August, and this year marks 70 years since British India split into two nations Hindu-majority India and Muslim-majority Pakistan and millions were uprooted in one of the largest mass migrations in history

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A demonstrator holds up a picture of Heather Heyer during a demonstration in front of City Hall for victims of the Charlottesville, Virginia tragedy, and against racism in Los Angeles, California, USA. Rallies have been planned across the United States to demonstrate opposition to the violence in Charlottesville

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Jessica Mink (R) embraces Nicole Jones (L) during a vigil for those who were killed and injured when a car plowed into a crowd of anti-fascist counter-demonstrators marching near a downtown shopping area Charlottesville, Virginia

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White nationalists, neo-Nazis and members of the alt-right clash with counter-protesters as they enter Lee Park during the Unite the Right in Charlottesville, Virginia. After clashes with anti-fascist protesters and police the rally was declared an unlawful gathering and people were forced out of Lee Park

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A North Korean flag is seen on top of a tower at the propaganda village of Gijungdong in North Korea, as a South Korean flag flutters in the wind in this picture taken near the border area near the demilitarised zone separating the two Koreas in Paju, South Korea

Reuters

A firefighter extinguishes flames as a fire engulfs an informal settlers area beside a river in Manila

AFP

A rally in support of North Korea's stance against the US, on Kim Il-Sung square in Pyongyang.

AFP

Rocks from the collapsed wall of a hotel building cover a car after an earthquake outside Jiuzhaigou, Sichuan province

Reuters

People in Seoul, South Korea walk by a local news program with an image of US President Donald Trump on Wednesday 9 August. North Korea and the United States traded escalating threats, with Mr Trump threatening Pyongyang with fire and fury like the world has never seen

A Maasai woman waits in line to vote in Lele, 130 km (80 miles) south of Nairobi, Kenya. Kenyans are going to the polls today to vote in a general election after a tightly-fought presidential race between incumbent President Uhuru Kenyatta and main opposition leader Raila Odinga

Pro-government supporters march in Caracas, Venezuela on 7 August

Reuters

Children pray after releasing paper lanterns on the Motoyasu river facing the Atomic Bomb Dome in remembrance of atomic bomb victims on the 72nd anniversary of the bombing of Hiroshima, western Japan.

REUTERS

Russian President Vladimir Putin (L), accompanied by defence minister Sergei Shoigu, gestures as he fishes in the remote Tuva region in southern Siberia.

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A family claiming to be from Haiti drag their luggage over the US-Canada border into Canada from Champlain, New York, U.S. August 3, 2017.

Reuters

A disabled man prepares to cast his vote at a polling station in Kigali, Rwanda, August 4, 2017

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ATTENTION EDITORS -People carry the body of Yawar Nissar, a suspected militant, who according to local media was killed during a gun battle with Indian security forces at Herpora village, during his funeral in south Kashmir's Anantnag district August 4, 2017.

Reuters

A general view shows a flooded area in Sakon Nakhon province, Thailand August 4, 2017.

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A plane landed in Sao Joao Beach, killing two people, in Costa da Caparica, Portugal August 2, 2017

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Hermitage Capital CEO William Browder waits to testify before a continuation of Senate Judiciary Committee hearing on alleged Russian meddling in the 2016 presidential election on Capitol Hill in Washington, U.S., July 27, 2017

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TOPSHOT - Moto taxi driver hold flags of the governing Rwanda Patriotic Front's at the beginning of a parade in Kigali, on August 02, 2017. Incumbent Rwandan President Paul Kagame will close his electoral campaigning ahead of the August 4, presidential elections which he is widely expected to win giving him a third term in office

AFP

TOPSHOT - Migrants wait to be rescued by the Aquarius rescue ship run by non-governmental organisations (NGO) "SOS Mediterranee" and "Medecins Sans Frontieres" (Doctors Without Borders) in the Mediterranean Sea, 30 nautic miles from the Libyan coast, on August 2, 2017.

AFP

Two children hold a placard picturing a plane as they take part in a demonstration in central Athens outside the German embassy with others refugees and migrants to protest against the limitation of reunification of families in Germany, on August 2, 2017.

AFP

Flames erupt as clashes break out while the Constituent Assembly election is being carried out in Caracas, Venezuela, July 30, 2017. REUTERS/Carlos Garcia Rawlins

Reuters

People in the village of Gabarpora carry the remains of Akeel Ahmad Bhat, a civilian who according to local media died following clashes after two militants were killed in an encounter with Indian security forces in Hakripora in south Kashmir's Pulwama district, August 2, 2017. REUTERS/Danish Ismail

Reuters

- Incumbent Rwandan President Paul Kagame gestures as he arrives for the closing rally of the presidential campaign in Kigali, on August 2, 2017 while supporters greet him. Rwandans go the polls on August 4, 2017 in a presidential election in which strongman Paul Kagame is widely expected to cruise to a third term in office.

AFP

Soldiers of China's People's Liberation Army (PLA) get ready for the military parade to commemorate the 90th anniversary of the foundation of the army at Zhurihe military training base in Inner Mongolia Autonomous Region, China.

REUTERS

Cyclists at the start of the first stage of the Tour de Pologne cycling race, over 130km from Krakow's Main Market Square, Poland

EPA

Israeli border guards keep watch as Palestinian Muslim worshippers pray outside Jerusalem's old city overlooking the Al-Aqsa mosque compound

Ahmad Gharabli/AFP

A supporter of Pakistan's Prime Minister Nawaz Sharif passes out after the Supreme Court's decision to disqualify Sharif in Lahore

Reuters/Mohsin Raza

Australian police officers participate in a training scenario called an 'Armed Offender/Emergency Exercise' held at an international passenger terminal located on Sydney Harbour

Reuters/David Gray

North Korean soldiers watch the south side as the United Nations Command officials visit after a commemorative ceremony for the 64th anniversary of the Korean armistice at the truce village of Panmunjom in the Demilitarized Zone (DMZ) dividing the two Koreas

Reuters/Jung Yeon-Je

Bangladeshi commuters use a rickshaw to cross a flooded street amid heavy rainfall in Dhaka. Bangladesh is experiencing downpours following a depression forming in the Bay of Bengal.

Munir Uz Zaman/AFP

The Soyuz MS-05 spacecraft for the next International Space Station (ISS) crew of Paolo Nespoli of Italy, Sergey Ryazanskiy of Russia, and Randy Bresnik of the U.S., is transported from an assembling hangar to the launchpad ahead of its upcoming launch, at the Baikonur Cosmodrome in Baikonur, Kazakhstan

Reuters/Shamil Zhumatov

A protester shouts at U.S. President Donald Trump as he is removed from his rally with supporters in an arena in Youngstown, Ohio

Reuters

Indian supporters of Gorkhaland chant slogans tied with chains during a protest march in capital New Delhi. Eastern India's hill resort of Darjeeling has been rattled at the height of tourist season after violent clashes broke out between police and hundreds of protesters of the Gorkha Janmukti Morcha (GJM) a long-simmering separatist movement that has long called for a separate state for ethnic Gorkhas in West Bengal. The GJM wants a new, separate state of "Gorkhaland" carved out of eastern West Bengal state, of which Darjeeling is a part.

Sajjad Hussain/AFP/Getty Images

Demonstrators clash with riot security forces while rallying against Venezuela's President Nicolas Maduro's government in Caracas, Venezuela. The banner on the bridge reads "It will be worth it"

Reuters

The Heathcote river as it rises to high levels in Christchurch, New Zealand. Heavy rain across the South Island in the last 24 hours has caused widespread damage and flooding with Dunedin, Waitaki, Timaru and the wider Otago region declaring a state of emergency.

Getty Images

A mourner prays at a memorial during an event to commemorate the first anniversary of the shooting spree that one year ago left ten people dead, including the shooter in Munich, Germany. One year ago 18-year-old student David S. shot nine people dead and injured four others at and near a McDonalds restaurant and the Olympia Einkaufszentrum shopping center. After a city-wide manhunt that caused mass panic and injuries David S. shot himself in a park. According to police David S., who had dual German and Iranian citizenship, had a history of mental troubles.

Getty

Palestinians react following tear gas that was shot by Israeli forces after Friday prayer on a street outside Jerusalem's Old City

Reuters/Ammar Awad

Ousted former Thai prime minister Yingluck Shinawatra greets supporters as she arrives at the Supreme Court in Bangkok, Thailand

Reuters/Athit Perawongmetha

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AI is coming to war, regardless of Elon Musk's well-meaning concern - The Independent

Amwell CMO: Google partnership will focus on AI, machine learning to expand into new markets – FierceHealthcare

Posted on October 30, 2020 by Danzig

Amwell is looking to evolve virtual care beyond just imitating in-person care.

To do that, the telehealth companyexpects to use its latestpartnership with Google Cloud toenable it to tap into artificial intelligence and machine learning technologies to create a better healthcare experience, according to Peter Antall, M.D., Amwell's chief medical officer.

"We have a shared vision to advance universal access to care thats cost-effective. We have a shared vision to expand beyond our borders to look at other markets. Ultimately, its a strategic technology collaboration that were most interested in," Antall said of the company's partnership with the tech giant during a STATvirtual event Tuesday.

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"What we bring to the table is that we can help provide applications for those technologiesthat will have meaningful effects on consumers and providers," he said.

The use of AI and machine learning can improve bot-based interactions or decision support for providers, he said. The two companies also want to explore the use of natural language processing and automated translation to provide more "value to clients and consumers," he said.

Joining a rush of healthcare technology IPOs in 2020, Amwell went public in August, raising$742 million. Google Cloud and Amwell also announced amultiyear strategic partnership aimed at expanding access to virtual care, accompanied by a$100 million investmentfrom Google.

During an HLTH virtual event earlier this month, Google Cloud director of healthcare solutions Aashima Gupta said cloud and artificial intelligence will "revolutionize telemedicine as we know it."

RELATED:Amwell files to go public with $100M boost from Google

"There's a collective realization in the industry that the future will not look like the past," said Gupta during the HTLH panel.

During the STAT event, Antall said Amwellis putting a big focus onvirtual primary care, which has become an area of interest for health plans and employers.

"It seems to be the next big frontier. Weve been working on it for three years, and were very excited. So much of healthcare is ongoing chronic conditions and so much of the healthcare spend is taking care ofchronic conditionsandtaking care of those conditions in the right care setting and not in the emergency department," he said.

The companyworks with 55 health plans, which support over 36,000 employers and collectively represent more than 80million covered lives, as well as 150 of the nations largest health systems. To date, Amwell says it has powered over 5.6million telehealth visits for its clients, including more than 2.9million in the six months ended June 30, 2020.

Amwell is interested in interacting with patients beyond telehealth visits through what Antall called "nudges" and synchronous communication to encouragecompliance with healthy behaviors, he said.

RELATED:Amwell CEOs on the telehealth boom and why it will 'democratize' healthcare

It's an area where Livongo, recently acquired by Amwell competitor Teladoc,has become the category leader by using digital health tools to help with chronic condition management.

"Were moving into similar areas, but doing it in a slightly different matter interms of how we address ongoing continuity of care and how we address certain disease states and overall wellness," Antallsaid, in reference to Livongo's capabilities.

The telehealth company also wants to expand into home healthcare through the integration of telehealth and remote care devices.

Virtual care companies have been actively pursuing deals to build out their service and product lines as the use of telehealth soars. To this end, Amwell recently deepened its relationship with remote device company Tyto Care. Through the partnership, the TytoHome handheld examination device that allows patients to exam their heart, lungs, skin, ears, abdomen, and throat at home, is nowpaired withAmwells telehealth platform.

Looking forward, there is the potential for patients to getlab testing, diagnostic testing, and virtual visits with physicians all at home, Antall said.

"I think were going to see a real revolution in terms ofhow much more we can do in the home going forward," he said.

RELATED:Amwell's stock jumps on speculation of potential UnitedHealth deal: media report

Amwell also is exploring the use of televisions in the home to interact with patients, he said.

"We've done work with some partners and we're working toward a future where, if it's easier for you to click your remote and initiate a telehealth visit that way, thats one option. In some populations, particularly the elderly, a TV could serve as a remote patient device where a doctor or nurse could proactively 'ring the doorbell' on the TV and askto check on the patient," Antall said.

"Its video technology that'salready there in most homes, you just need a camera to go with it and a little bit of software.Its one part of our strategy to be available for the whole spectrum of care and be able to interact in a variety of ways," he said.

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Amwell CMO: Google partnership will focus on AI, machine learning to expand into new markets - FierceHealthcare

Element AI, a platform for companies to build AI solutions, raises … – TechCrunch

Posted on June 16, 2017 by Danzig

The race for artificial intelligence technology is on, and while tech giants like Google and Facebook snap up top talent to build out their own AI-powered products, a new startup has just raised a huge round of funding to help the rest of us.

Element AI a Montreal-based platform and incubator that wants to be the go-to place for any and all companies (big or small) that are building or want to include AI solutions in their businesses, but lack the talent and other resources to get started is announcing a mammoth Series A round of $102 million. It plans to use the funding for hiring talent, for business development, and also, to put some money where its mouth is, by selectively investing in some of the solutions that will be built within its doors.

Our goal remains to lower the barrier for entry for commercial applications in AI, said Jean-Franois Gagn, the CEO of Element AI, in an interview. Everyone wants to have these capabilities, its hard for most companies to pull it off because of the lack of talent or access to AI technology. That is the opportunity. The company currently has 105 employees and the plan is to ramp that up to 250 in the next couple of months, he said.

The round was led by the prolific investorData Collective, with participation from a wide range of key financial and strategic backers. They include Fidelity Investments Canada, Koreas Hanwha, Intel Capital, Microsoft Ventures, National Bank of Canada, NVIDIA, Real Ventures, and several of the worlds largest sovereign wealth funds.

This large Series A has been swift: it comes only six months after Element AI announced a seed round from Microsoft Ventures (of an undisclosed amount), and only eight months after the company launched.

Weve asked Gagn and Element AIs investors, but no one is disclosing the valuation.However, what we do know is that the startup already has several companies signed up as customers and working on paid projects; and it has hundreds of potential companies on its list for more work.

As weve been engaging with corporates and startups [to be in our incubator] we have realized that being engaged in both at the same time is not easy, Gagn said. Weve started to put together a business network, including taking positions in startups to help them by investing capital, resources, providing them with technology and bringing them all the tools they need to accelerate the development of their apps and help them connect with large corporates who are their customers. The aim is to back up to 50 startups in the field, he said.

The strategic investors also fit into different parts of Element AIs business funnel. Some like Nvidia are working as partners for business in its case, using its deep learning platform, according to Jeff Herbst, VP of business development for NVIDIA. Element AI will benefit by continuing to leverage NVIDIAs high performance GPUs and software at large scale to solve some of the worlds most challenging issues, he said in a statement. Others, likeHanwha, are coming in as customer-investors, there to take advantage of some of the smarts.

AI in its early days may have been the domain of tech companies like Google, Apple and IBM when it came to needing and commercializing it, but these days, the wide range of solutions that can be thought of as AI-based, and applications for it, can touch any and all aspects of a business, from back-office functions and customer-facing systems, through to cybersecurity and financial transactions, to manufacturing, logistics and transportation, and robotics.

But the big issue has been that up to now, the most innovative startups in these areas are getting snapped up by the large tech giants (sometimes directly from the universities where they form, sometimes a bit later).

Then consider those that are independent and arent getting acquired (yet). There still remains a gap for most companies between what skills are out on the market to be used, and what would be the most useful takeaway for their own businesses.

In other words, many considering how to use AI in their businesses are effectively starting from scratch. Longer term, that disparity between the AI haves and have-nots could prove to be disastrous for the idea of democratising intellectual power and all the spoils that come with it.

There is not a lot left in the middle, Data Collectives Matt Ocko said in an interview. The issue with corporations, governments and others trapped in that no mans land of AI have-nots is that their rivals with superior AI-powered decision making and signal processing will dominate global markets.

The idea of building an AI incubator or safe space where companies that might even sometimes compete against each other, are now sitting alongside each other talking to the same engineers to build their new products, may be an industry first.

But the basic model is not: Element AI is tackling this problem essentially by leaning on trends in outsourcing: systems integrators, business process outsourcers, and others have built multi-billion dollar businesses by providing consultancy or even fully taking the reins on projects that businesses do not consider their core competency.

The same is happening here.Element AI says that initial products that can be picked up there include predictive modeling, forecasting models for small data sets, conversational AI and natural language processing, image recognition and automatic tagging of attributes based on images, aggregation techniques based on machine learning, reinforcement learning for physics-based motion control, compression of time-series data, statistical machine learning algorithms, voice recognition, recommendation systems, fluid simulation, consumer engagement optimization and computational advertising.

I asked, and I was told multiple times, that essentially colocating their R&D next to other first, for now, is not posing a problem for the companies who are getting involved. If anything, for those who understand the big-data aspect of AI intelligence, they can see that the benefit for one will indirectly benefit the rest, and speed everything up.

That model is what made Yoshua Bengio the godfather of machine learning so excited about co-founding this company, Ocko said. That massive research advantage leads Element AI to be able to deliver technically advantaged, increasingly cost effective solutions. It means they dont have to treat AI decision making capability as a scare resource, wielded like a club on everyone else.

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Element AI, a platform for companies to build AI solutions, raises ... - TechCrunch

Anthropic is the new AI research outfit from OpenAIs Dario Amodei, and it has $124M to burn – TechCrunch

Posted on May 29, 2021 by Danzig

As AI has grown from a menagerie of research projects to include a handful of titanic, industry-powering models like GPT-3, there is a need for the sector to evolve or so thinks Dario Amodei, former VP of research at OpenAI, who struck out on his own to create a new company a few months ago. Anthropic, as its called, was founded with his sister Daniela and its goal is to create large-scale AI systems that are steerable, interpretable, and robust.

The challenge the siblings Amodei are tackling is simply that these AI models, while incredibly powerful, are not well understood. GPT-3, which they worked on, is an astonishingly versatile language system that can produce extremely convincing text in practically any style, and on any topic.

But say you had it generate rhyming couplets with Shakespeare and Pope as examples. How does it do it? What is it thinking? Which knob would you tweak, which dial would you turn, to make it more melancholy, less romantic, or limit its diction and lexicon in specific ways? Certainly there are parameters to change here and there, but really no one knows exactly how this extremely convincing language sausage is being made.

Its one thing to not know when an AI model is generating poetry, quite another when the model is watching a department store for suspicious behavior, or fetching legal precedents for a judge about to pass down a sentence. Today the general rule is: the more powerful the system, the harder it is to explain its actions. Thats not exactly a good trend.

Large, general systems of today can have significant benefits, but can also be unpredictable, unreliable, and opaque: our goal is to make progress on these issues, reads the companys self-description. For now, were primarily focused on research towards these goals; down the road, we foresee many opportunities for our work to create value commercially and for public benefit.

The goal seems to be to integrate safety principles into the existing priority system of AI development that generally favors efficiency and power. Like any other industry, its easier and more effective to incorporate something from the beginning than to bolt it on at the end. Attempting to make some of the biggest models out there able to be picked apart and understood may be more work than building them in the first place. Anthropic seems to be starting fresh.

Anthropics goal is to make the fundamental research advances that will let us build more capable, general, and reliable AI systems, then deploy these systems in a way that benefits people, said Dario Amodei, CEO of the new venture, in a short post announcing the company and its $124 million in funding.

That funding, by the way, is as star-studded as you might expect. It was led by Skype co-founder Jaan Tallinn, and included James McClave, Dustin Moskovitz, Eric Schmidt and the Center for Emerging Risk Research, among others.

The company is a public benefit corporation, and the plan for now, as the limited information on the site suggests, is to remain heads-down on researching these fundamental questions of how to make large models more tractable and interpretable. We can expect more information later this year, perhaps, as the mission and team coalesces and initial results pan out.

The name, incidentally, is adjacent to anthropocentric, and concerns relevancy to human experience or existence. Perhaps it derives from the Anthropic principle, the notion that intelligent life is possible in the universe because well, were here. If intelligence is inevitable under the right conditions, the company just has to create those conditions.

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Anthropic is the new AI research outfit from OpenAIs Dario Amodei, and it has $124M to burn - TechCrunch

What these teen tech entrepreneurs think about Microsoft, Amazon, Apple, AI, the cloud and more – GeekWire

Posted on August 2, 2017 by Danzig

Atul Ajoy, left, and Michael Royzen, right, are high school students and software developers whove launched their own startups. (GeekWire Photo / Todd Bishop)

Michael Royzen and Atul Ajoy have several things in common, besides being high-school students in the Seattle region.

Theyre both entrepreneurs and software developers who, despite being in their teens, have already launched their own technology startups and projects. And theyve both been invited to major software development conferences: Royzen, 17, attended Apples WWDC in San Francisco and Ajoy, 15, went to Microsoft Build in Seattle.

They had never met before, but it struck us that they would have a lot to talk about, and some interesting insights to share based on their similar but separate experiences. So we brought them together to talk about their experiences on the GeekWire podcast. And we werent disappointed.

Listen below, download the MP3, and keep reading for highlights.

Atul Ajoy will be a 10th grader next year at Redmond High School. A tech enthusiast and frequent blogger,hes the founder of a startup called Chromata, which is reimagining school fundraising with artificial intelligence, machine learning, and blockchain. He wrote about his experience attending Microsoft Build in a guest post on GeekWire earlier this year.

Michael Royzen will be a senior at the The Bush School. Hes a software developer and entrepreneur who founded and serves as CEO of his own company, Mlab Technologies, Inc., which makes apps for Apple platforms, including Rydeand RecipeReadr. A past GeekWire Geek of the Week, he attended Apples Worldwide Developer conference at Apples invitation.

Given their respective backgrounds and platforms of choice, we originally thought this might be a Microsoft vs. Apple conversation, but Royzen is actually interning this summer at Microsoft Research, giving him an understanding and appreciation for the Redmond tech giant, as well.

So at a time when many of their peers are focused on the Snapchats and Instagrams of the world, why are Microsoft and Apple relevant to these teen entrepreneurs? A big part of the answer is their developer platforms.

Microsofts really relevant today because not only do they care about their first-party applications, but their cloud platform Azure and then other services that they provide really help third-party companies even my own startup to get started and build products that anyone in the world can use, Ajoy said. Microsoft doesnt get enough credit for this, but theyre doing a lot of cool things that enable the next generation of technology.

He cited, as an example, the Microsoft HoloLens mixed reality headset, which he got a chance to experience first-hand at Microsoft Build.

Royzen agreed with many of Ajoys comments about Microsoft, citing the companys huge, huge focus on the cloud and artificial intelligence.

Apple, I think is a very different company, he said. Apple is mostly a hardware company thats focused on selling to consumers. They really ride waves of popular culture. One notable difference, as he pointed out, is that Apple is doing machine-learning processing on-device vs. in the cloud.

They both use Amazon Web Services, thanks to AWS credits they were able to get as students. But they said theyre also impressed with the ease-of-use, rapid release schedules and features of Microsoft Azure and Google Cloud.

Theyre also optimistic about artificial intelligence and expressed optimism that humanity can avoid the doomsday AI scenarios.

There is reason to be wary of whats coming, Ajoy acknowledged, but I think that if we band together and are responsible about what we do, well see new companies form that are just as successful as these, and see that technology can transform our lives, the way we live, the way we communicate, and the way we almost do anything.

I think it will be a smooth transition, just like it was from no Internet to Internet. Perhaps it will be quicker, but it wont be as if you wake up one day and robots have taken over the entire world, unless were incredibly irresponsible about AI. It will be a gradual transition, one towards a world where everything is more automated and hopefully people are more productive.

What advice would they give to other teens who want to get started in software development or startups?

Follow your passion, Royzen said. If you have this inkling of something you want to get started, that means theres something in you thats driving you.

What really helped me is to look inside and figure out why you want to do this, and to use that as a starting point. For me, I was really fascinated by iOS, Apples consumer devices, and I wanted to see how I could help people by creating software solutions for those devices, he said.

Ajoy agreed with that sentiment: Dont try until you know that you have a passion for it, he said. Once you do, its really easy to get to any point you want, because once you set your goal for yourself, if you want to do it, you can get there.

If you like what you do, it will become fun rather than hard, he said. Make sure this is your passion, decide what your passion is, and then follow it relentlessly, and youll definitely get as far as you want to go.

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What these teen tech entrepreneurs think about Microsoft, Amazon, Apple, AI, the cloud and more - GeekWire

Why Artificial Intelligence Should Be on the Menu this Season – FSR magazine

Posted on October 1, 2020 by Danzig

The perfect blend of AI collaboration needs workers to focus on the tasks where they excel.

Faced with the business impacts of one of the largest health crises to date, restaurants of all sizes are in a pivotal moment in time where every decisionshort term and long termcounts. For their businesses to survive, restaurant owners have had to act fast by rethinking operations and introducing pandemic-related initiatives.

Watching the worlds largest chains all the way down to the local mom-and-pops become innovators in such extreme times has shown the industrys tenacity and survival instinct, even when all odds are stacked against their favor. None of these initiatives would be possible without technology as the driving factor.

Why AI is on the Menu This Season

A recent Dragontail Systems survey found that 70 percent of respondents would be more comfortable with delivery if they were able to monitor their orders preparation from start to finish. Consumers want to be at the forefront of their meals creationthey dont want to cook it, but they do want to know it was prepared in a safe environment and delivered hot and fresh to their door.

Aside from AIs role on the back-end helping with preparation time estimation and driver scheduling, the technology is now being used in cameras, for example, which share real-time images with consumers so that they can be sure their orders are handled with care. Amid the pandemic, this means making sure that gloves and masks are used during the preparation process and that workspaces are properly sanitized.

It is clear that AI is already radically altering how work gets done in and out of the kitchen. Fearmongers often tout AIs ability to automate processes and make better decisions in faster time compared to humans, but restaurants that deploy it mainly to displace employees will see only short-term productivity gains.

The perfect blend of AI collaboration needs workers to focus on the tasks where they excel, like customer service, so that the human element of the experience is never lost, only augmented.

AI on the Back-End

Ask any store or shift manager how they feel about workforce scheduling, and almost none will say its their favorite part of the job. Its a Catch-22: even when its done, its never perfect. However, when AI is in charge, everything looks different.

Parameters such as roles in the restaurants, peak days and hours, special events such as a Presidential debate, overtime, seniority, skills, days-off and more can be easily tracked. Managers are not only saving time in handing off this daunting task, but also allowing the best decisions to be made for optimal restaurant efficiency.

Another aspect is order prioritizationby nature, most kitchens and restaurants prepare meals based on FIFO (first-in-first-out). When using AI that enhances kitchen prioritization, for example, cooks are informed when to cook an order, ensuring that there are actually drivers available to deliver it to the customer in a timely manner.

Delivery management then allows drivers to make more deliveries per hour just by following the systems decisions, which improve and optimize the dispatching functionality.

The Birth of the Pandemic Intelligent Kitchen/Store

With the pandemic, our awareness of sanitation and cleanliness went dramatically up and the demand for solutions came with it. AI cameras give customers exactly thata real-time, never-before-seen view inside the kitchen to monitor how their order is being prepped, managed, and delivered.

Another aspect where AI comes in handy is avoiding dine-in and doing more take-out and drive-thru. When a customer is making an order online and picking the order up in their car, an AI camera can detect the car plate number in addition to the customer location (phone GPS) when entering the drive-thru area to provide a faster service with a runner from the restaurant.

In addition, the new concept of contactless menus where the whole menu is online with a quick scan of a QR code is another element building popularity during the pandemic. The benefits go beyond minimizing contact with physical menus; when a restaurant implements a smart online menu, they can collect data and offer personalized suggestions based on customers favorite foods, food/drink combos, weather-based food recommendations, upsell, cross-sell personalized etc.all powered by AI.

Restaurants can no Longer Afford Aversion to Technology

Challenges associated with technology, including implementation and a long-roadmap, are fading awaymost technology providers are offering Plug & Play products or services, and most of them are working on a SaaS model. This means theres no commitment, they are easy to use, and integrate seamlessly with the POS.

Restaurants dont have to make a big investment to reap the benefits technology bringstaking little steps that slowly improve restaurant operations and customer experience can still lead to increased growth and higher profit margins, especially during the pandemic when money is tight.

Technology enhances the experience, giving consumers a reason to keep ordering from their favorite places at a time when the stakes have never been so high, and the competition has never been as fierce. The pandemic is far from over but the changes we are seeing will be here for a lifetime. Thats why it is so important to leverage technology and AI now in order to see improvements in customer satisfaction and restaurant efficiency in the long term.

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Why Artificial Intelligence Should Be on the Menu this Season - FSR magazine

AI Models to Help Identify Invasive Species of Plants Across the UK – Unite.AI

Posted on June 21, 2020 by Danzig

We sat down (virtually) with Patrick Dorsey, the Vice President of Product Marketing, Programmable Solutions Group, Intel and Jason Mitchell, a managing director in Accentures Communications, Media & Technology practice and the companys client lead for Intel.

We discussed how on Earth Day 2020,Accenture,Inteland theSulubaa Environmental Foundation decided to partner to use artificial intelligence (AI) powered solution to monitor, characterize and analyze coral reef resiliency in a new collaborative project called CORail.

On Earth Day 2020, project CORaiL was announced, what was it about this project that caused you to take notice?

Jason Mitchell: Coral reefs are some of the worlds most diverse ecosystems, with more than eight hundred species of corals building and providing habitats and shelter for approximately 25% of global marine life. The reefs also benefit humans protecting coastlines from tropical storms, providing food and income for 1 billion people, and generating US$9.6 billion in tourism and recreation annually. But reefs are being endangered and rapidly degraded by overfishing, bottom trawling, warming temperatures and unsustainable coastal development. This project allowed Accenture and our ecosystem partners to apply intelligence to the preservation and rebuilding of this precious ecology and measure our success in a non-intrusive way.

Could you describe some of the technology at Intel that is being used in the underwater video cameras?

Patrick Dorsey: The underwater cameras areequipped withtheAccentureApplied IntelligenceVideo Analytics Services Platform (VASP)to detect and photograph fish as they pass. VASP usesAI to count and classify the marine life, with the data then sent to a surface dashboard,where it providesanalytics and trends to researchers in realtime, enabling them to makedata-driven decisionsto protect the coral reef.AccenturesVASP solution ispowered byIntelXeonprocessors, IntelFPGA Programmable Acceleration Cards,anIntelMovidiusVPUand the IntelDistribution ofOpenVINOtoolkit.

Work is currently being undertaken on the next-generation CORaiL prototype. What advanced features will this prototype have compared to the current version of CORaiL?

Jason Mitchell: We are scaling our work in the Philippines with a next-gen Project: CORaiL prototype, which will include an optimized convolutional neural network and a backup power supply. We are also lookinginto infra-red cameras which will enable videos at night to create a complete picture of the coral ecosystem. These technology advances will allow our solution to scale to look at new use cases like: studying the migration rate of tropical fish to colder countries and monitoring intrusion in protected or restricted underwater areas.

Could you share some of the computer vision challenges that are involved in monitoring different fish populations in an underwater setting which may result in significant changes in lighting conditions?

Patrick Dorsey: A critical element of Project: CORaiL is to identify the number and variety of fish around a reef, which serve as an important indicator of overall reef health. Traditional coral reef monitoring efforts involve human divers manually capturing video footage and photos of the reef, which is dangerous and time-intensive and can disrupt marine life, as divers might inadvertently frighten fish into hiding.

CORaiL monitors coral reef health in the Philippines, are there plans on expanding to other regions?

Jason Mitchell: Its still early days with this technology, so were currently focused on the reef surrounding the Pangatalan Island in the Philippines.

Is there anything else that you would like to share about CORaiL?

Jason Mitchell: AI should be an added contributor to how people perform their work, rather than a backstop for automation. For Project: CORaiL, AI is empowering our engineers to achieve more and learn faster when it comes to growing the coral reef. It empowers the solution to gather data in a non-intrusive manner, allowing the scientists and data engineers to gather data from the reef with minimal disruption to this fragile ecology.

What are some of the other ways AI is being used for Social Good?

Patrick Dorsey: At Intel, we are working with partners to use AI tocurb anti-poaching of endangered animals, tomap vulnerable populations, tohelp the quadriplegic community regain mobilityand more. We are deeply committed to advancing uses of AI that most positively impact the world.

Jason Mitchell: Throughour Responsible AI practice at Accenture, we help organizations implement governance frameworks and tools to ensure theyre deployingAI in a way that aligns to their corporate values and mitigates unintended consequences.

I would like to thank both of you for taking the time to explain why Accenture,Intel chose to collaborate on this mission to save one of earths most precious resources.

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AI Models to Help Identify Invasive Species of Plants Across the UK - Unite.AI

AI Techniques Now Power All Facebook Translations – Fortune

Posted on August 6, 2017 by Danzig

When Facebook shows you an automated translation of someone's post, the tech behind that translation is now based entirely on neural networks essentially, brain-like systems that are among the building blocks of today's artificial intelligence efforts.

Facebook announced the move Thursday. Previously, it had been using a combination of technologies, also including good old-fashioned phrase-based machine translation models.

As the company noted, phrase-based machine translation doesn't work so well when translating between languages that order words in very different ways, because the technique relies on breaking sentences down into phrases.

The neural network model, on the other hand, makes it possible to "take into account the entire context of the source sentence and everything generated so far, to create more accurate and fluent translations," wrote Facebook's Juan Miguel Pino, Alexander Sidorov and Necip Fazil Ayan.

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That essentially makes for "more accurate and fluent translations," they wrote, noting an average relative increase of 11 percent in a commonly-used metric used for scoring the accuracy of machine translations.

Neural networks are computing systems that simulate the highly interconnected, flexible nature of biological brains in order to "think" in a similar way to how we think. They're very useful for image and speech recognition, and other applications where you might get better results from training a system to learn for itself than from giving it set rules.

Google also uses neural networks to power some of the machine translations in its Google Translate service. For both companies, as well as rivals such as Microsoft , Apple and Amazon , the ability for their nascent AI technologies to understand context is crucial to their hopes for building human-like virtual assistants, bots and other futuristic interfaces for their users.