2018 was a year of reckoning for artificial intelligence (AI), proving that it's here to stay and will soon be all around us. Although the hype was at an all-time high, Deloitte’s State of AI in the Enterprise 2018 report showed that 82% of early adopters of AI saw positive ROI, and 63% adopted machine learning (ML) as a key technology in 2018.

In 2019, we can expect more serious adoption and value creation for businesses with AI. Based on hundreds of conversations with practitioners and thought leaders, and after many AI projects this year, here's my take on what to expect in 2019.

AI Will Be Omnipresent In Most Human Interactions 

AI has proven itself a powerful human capacity augmenter, powering progress and productivity at many levels in the workplace and at home. With a good part of our lives digitally connected or even controlled, AI will become the augmented way to elevate human experiences through the apps we use, the customer support calls we make and the cars we drive. AI will be leveraged by brands to deliver a highly tailored and personalized experience even in non-digital environments.


Early Adopters Emerge As New Leaders; Laggards Fall Behind

AI is a new class of technology where the fast followers may never catch up. The trend of how systems built with machine learning and AI are constantly learning and constantly improving will only continue to grow with time, so starting late may mean never catching up. Harvard Business Review (HBR) published an article on this challenge, indicating that unlike traditional informational technology innovations, organizations will need more time to ramp up and begin using artificial intelligence to deliver value to business processes.

Value Realization Models With AI Will Go Mainstream

One of the key observations in my personal experience in the industry is that more projects that start with a solution to a business problem delivered more value in the enterprise in 2018. This is in stark contrast to most AI projects, where organizations start with data or models and see what they can do with them, experimenting their way into AI. The reason this takes a while is because most folks who are doing machine learning and data science are really not enterprise IT folks who know what it takes to deploy, manage, measure and integrate into the complex enterprise ecosystems. Focusing on the business process and then sourcing the right building blocks to build an enterprise-grade AI will become the winning formula.

Investments In RPA Will Require AI To Deliver On The Promise

One of the technologies that has capitalized on the promise of AI to power productivity is robotic process automation (RPA). Deloitte's third annual RPA survey shows that even though a large number of enterprises have already started their RPA journeys, only 3% of them have seen any kind of real scale with robotics (over 50 robots in production). Intelligence automation with AI/ML used in conjunction with RPA will be the key to realizing value at scale. We will see RPA vendors trying to develop AI capabilities to address this and partner with AI companies to deliver the promise of RPA at scale with AI to enterprises.

Data Remains The Challenge And The Opportunity With AI 

Even though we create 2.5 quintillion bytes of data every day, according to IBM, useful and good data is still uncommon. It's a gaping hole being exposed by the explosion of machine learning in enterprises over the past few years. While this creates a new class of talent needed to find the needle in the data stack to power your machine learning projects, this part of the problem will continue to persist in 2019, as foundational data mining methods for use of data for ML haven't evolved yet. While core low-code/no-code machine learning models and tools are becoming more available, enterprises will have to create and curate useful data to power the models. In a weird way, data -- not ML -- will become a differentiator again for enterprises.

Talent Remains A Barrier To Adoption, But Democratization Is On Its Way

ML technologies require very highly specialized talent around statistics, math, programming and domain/industry expertise. We regularly saw companies fork over huge amounts of money (some even gave over a million dollars) for good ML talent in 2018, but these talents were likely lured and hired away by the top 1% of the organizations. The rest of the enterprises stood little chance in hiring this quality of talent. In 2019, we will see the democratization of talent needed to deliver ML projects, including ready-to-use ML models from leading cloud vendors, auto-ML tools from AWS or GCP that make model selection/deployment easier and developer-focused platforms to build and orchestrate enterprise AI systems.

AI Will Continue To Create New-Collar Jobs

There was a time when job titles such as social media manager and growth marketer didn’t exist. The advent of the internet and then social media has brought about a plethora of opportunities in the last two decades. AI will get there sooner than later, creating an entirely new class of jobs that will involve training and augmenting machines with the human factor. The gig economy will further enable and accelerate this move. I've written about this in the past in regard to financial services.

AI Gets Regulated

Facebook and Google have been dominating headlines lately on their use of user data. With the issues of data rights, use of personal data and bias coming to the surface, there is a plethora of issues that will need to be addressed in the digital age. Is personal data covered by property rights, and if so, should it be treated as such? This debate will open up 2019 as the year when progressive and smart governments will start working toward inclusive regulations that will alter the way organizations deploy AI systems to power their businesses. Responsible AI will take its form as a mainstream requirement for anyone seriously looking at AI as a strategic lever in the digital age.

Read Source Article: Forbes

#AI #EnterpriseAI #MachineLearning #DeepLearning #DataScience

Artificial Intelligence is either a silver bullet for every problem on the planet, or the guaranteed cause of the apocalypse, depending on whom you speak to.

The reality is likely to be far more mundane. AI is a tool, and like many technological breakthroughs before it, it will be used for good and for bad. But focusing on potential extreme scenarios doesn’t help with our present reality. AI is increasingly being used to influence the products we buy and the music and films we enjoy; to protect our money; and, controversially, to make hiring decisions and process criminal behaviour.

The major problem with AI is what’s known as ‘garbage in, garbage out’. We feed algorithms data that introduces existing biases, which then become self-fulfilling. In the case of recruitment, a firm that has historically hired male candidates will find that their AI rejects female candidates, as they don’t fit the mould of past successful applicants. In the case of crime or recidivism prediction, algorithms are picking up on historical and societal biases and further propagating them.

In some ways, it’s a chicken and egg problem. The Western world has been digitized for longer, so there are more records for AIs to parse. In addition, women have been under-represented in many walks of life, so there is less data, and what data exists is often of a lower quality. If we can’t feed AIs quality data that is free of bias, they will learn and continue the prejudices we seek to eliminate. Often the largest datasets available are also simply of such low quality that the results are unpredictable and unexpected, such as racist chatbots on Twitter.

None of this is to say that anything about AI is inherently bad. It has the potential to be a better decision-making tool than people. AI can’t be bribed, cheated or socially engineered. It doesn’t get tired, hungry or mad. So what’s the answer?

In the short term, we need to develop standards and testing for AI that enable us to identify bias and work against it. These need to be independently agreed upon and rigorously tested, as understanding what’s happening behind the scenes in machine learning is incredibly complex and difficult.

We also need to think about the user-facing experience of AI and the inherent sexism of personal assistants and chatbots that are, by and large, women. Whereas perhaps the first well-known automated assistant was male (HAL-9000), it seems that today we can only picture our automated assistants as a subservient woman. Often this is defended by consumer research demonstrating that consumers simply prefer a female voice, but how much of this is due to gender stereotypes around the types of role we use assistants for?

We have an opportunity to address these imbalances by driving a greater focus on inclusion, empowerment and equality. More women working in the technology industry, writing algorithms and feeding into product development will change how we imagine and develop technology, and how it sounds and looks.

However, it’s a tough nut to crack. Technology and engineering are historically two of the most male-dominated workforces on the planet, and unconscious gender bias (as well as a reasonable amount of conscious bias) is rampant. Even though we’ve seen that the presence of women on boards can bolster performance, generate new ideas and help companies weather times of crisis, often male candidates who have been CEOs at small firms are prioritized over women who have overseen entire divisions at large companies, and who arguably have greater and more relevant experience.

Good work is being done to introduce girls to technology skills, encourage them to pursue further education in STEM subjects and apply for roles in male-dominated fields, but until top-down change can be implemented, progress will be slow. There is a real risk that without intervention today, this next generation of women will be denied the opportunities we are skilling and training them for, because biased algorithms will simply sideline them in favour of propagating the status quo.

I am a great believer in the power of programmes such as Girls in AI to ensure the next generation of workers have the skills needed to create impactful change. But we need industry-level agreement in order to clear the path for them. We need standards and testing, both in terms of the quality of data we rely on and in terms of detecting bias, and a commitment to re-examine the policies and incentives in place at technology companies, in order to get more women onboard and accelerate the rate of change.

Read Source Article: World Economic Forum

#AI #MachineLearning #DeepLearning #Technology #GenderGaps

As part of our coverage to kick off 2019, The Robot Report has shared the top robotics stories, most-funded robotics companies of 2018, and 10 major robotics companies to watch in 2019.

We didn’t forget about the large amount of exciting robotics startups out there. We’re focusing on young companies. With the exception of Badger Technologies, which was acquired for an undisclosed amount by Jabil in mid-2017 after just six months in existence, and Kassow Robots, which is self-funded, none of the companies on the list have raised more than $6.8 million. And no company is older than five years.

It’s hard to narrow this list down to just 10 robotics startups, so please share in the comments some robotics startups you will be watching in 2019. No disrespect to the many other robotics startups doing tremendous work, such as IMS Systems, Motus Labs and Root AI, to name a few. Here, in alphabetical order, are 10 robotics startups The Robot Report will be watching in 2019.

10 robotics startups to watch in 2019

Augean Robotics
Headquarters: Philadelphia
Industry: Agricultural robotics
Founded: 2017
Funding: $250K Seed Round
Reason to watch: Augean Robotics makes Burro, an autonomous mobile robot that follows people on a farm, moving up to 500 lbs of cargo around to free up workers to perform more valuable tasks. Burro can learn the routes it takes and re-run them autonomously. Augean is currently working with fresh fruit farmers. In December 2018, Augean took home top honors at the FBNFarmers Startup Competition by winning the Judge’s Choice Award. Agriculture is a $5 trillion industry, and it’s ripe for automation.

Badger Technologies
Headquarters: Nicholasville, Kentucky
Industry: Mobile service robots
Founded: 2017
Reason to watch: According to Research and Markets, the retail automation sector is expected to be worth more than $18.9 billion by 2023. And Badger Technologies, which is owned by Jabil, is starting to carve out its own piece of that pie. The company just announced it is rolling out its Marty autonomous mobile robot to nearly 500 Giant/Martin and Stop & Stop stores after successful pilots. The rollout, one of the largest of its kind, will continue through the early part of 2019. Marty moves throughout stores looking for spills, obstacles, debris or anything that could impose potential safety risks to customers and store employees. Marty can also be used to address out-of-stock, planogram compliance, price integrity, and audit and compliance issues.

Headquarters: Stuttgart, Germany
Industry: Programming software
Founded: 2016
Funding: Seed round of 1 million-plus euros
Reason to watch: The two biggest obstacles that prevent SMEs for adopting industrial robots are the investment costs and required programming expertise. drag&bot GmbH, a spin-off of the Fraunhofer Institute for Manufacturing Engineering and Automation, is trying to solve the latter problem with drag-and-drop programming for industrial robots. We’ve heard this from others, but drag&bot want to make programming robots “as easy as operating a smartphone.” According to drag&bot, its software is most commonly used for robots handling parts, palletizing workpieces and loading or unloading machines. More complex tasks, including screwing or joining gears, are also possible. The software works independently of the robot hardware and currently supports ABB, Denso, Fanuc, Kuka and Universal Robots. drag&bot recently closed a Seed round of funding led by Speedinvest Industry, an Austria-based venture capitalist.

Dusty Robotics
Headquarters: Sunnyvale, Calif.
Industry: Construction Robotics
Founded: 2018
Funding: $2.2M
Reason to watch: Founded by former Savioke CTO Tessa Lau, Dusty Robotics wants to automate critical-path tasks on construction sites. When The Robot Report talked to Lau in the fall of 2018, she wasn’t yet ready to publicly share the tasks Dusty Robotics is looking to automate. The construction industry, as Lau pointed out in a recent blog, is in dire need of a technological shakeup. “I discovered that unlike industries such as manufacturing, which have seen consistent productivity growth due to process automation, productivity in the construction industry has actually declined over the past few decades … The average age of the construction workforce is 41, and anecdotally people were telling me that workers were switching jobs at 50 because their bodies just couldn’t handle the work anymore. At the same time, young people aren’t entering the workforce; younger construction workers declined by 30% in the decade leading up to 2016. Even aside from labor shortages, rework due to errors and miscommunication costs US construction companies an estimated $65B per year.”

Kassow Robots

Kassow Robots is developing 7-DOF cobot arms. (Credit: Kassow Robots)

Kassow Robots
Headquarters: Copenhagen, Denmark
Industry: Collaborative robots
Founded: 2014; launched products in 2018
Funding: Self-funded
Reason to watch: If Kassow Robots sounds familiar, it should. The company was co-founded by Kristian Kassow, one of the former co-founders of collaborative robotics leader Universal Robots. After about a five-year stint outside of collaborative robotics, Kassow is looking to make in-roads in an increasingly competitive market. To do so, Kassow Robots will be offering the KR810 (850mm reach, 10kg payload), KR1205 (1200mm reach, 5kg payload) and KR1805 (1800mm reach, 5kg payload). Each robot has built-in force torque sensors to detect impact and abnormal forces and stop the robots when they’re overloaded.

Realtime Robotics
Headquarters: Boston
Industry: Motion planning
Founded: 2016
Funding: $2M Seed Round
Reason to watch: Realtime Robotics, a spinout from Duke University, has developed a processor called “RapidPlan” that offers real-time motion planning for robotics and autonomous vehicles. According to Realtime, the processor can achieve sub-millisecond motion plans, is retargetable and updatable on the fly. Realtime said “a robot with fast reaction times can operate safely in an environment with humans. A robot that can plan quickly can be deployed in relatively unstructured factories and adjust to imprecise object locations and orientations, thus lowering a major the barrier to the use of robots.”

Headquarters: Odense, Denmark
Industry: Commercial Drones
Founded: 2014
Funding: $797K Seed Round
Reason to watch: Keep an eye on the Odense robotics cluster as a whole in 2019, but an interesting company watch is QuadSat. The commercial drone company autonomously tests and calibrates satellite and VSAT antennas. The company will initially focus on high-value aeronautical and maritime applications. QuadSAT was one of six companies to take part in Mission 1 of Seraphim Space Camp, the UK’s first accelerator for space-tech start-ups.

Two Danish robotics startups filed IPOs in 2018. Odico, a construction robotics company, in June 2018 became the first Danish robotics company to be admitted to trading on Nasdaq First North. Scape Technologies, which focuses on automated bin picking, began trading on Nasdaq First North Denmark in November. Michael Hansen, investment manager at Odense Seed & Venture, tells The Robot Report that the IPO culture is new in Denmark.

“Smaller companies went for direct investments or IPOs in Sweden. There has been a strong national political wish for a change of the culture,” he said. “The National and government-owned growth fund has supported the possibility of going IPO. This year (2018) we see the beginning of this new culture.”

Stocked Robotics
Headquarters: Austin, Texas
Industry: Fleet autonomy
Founded: 2017
Funding Status: Pre-seed
Reason to watch: Stocked Robotics is still semi-stealth mode, but it recently got its first customer up and running in June 2018. Stocked Robotics turns manually-driven forklifts into autonomous forklifts using its Stocked Intelligence Engine for Robot Automation (SIERA) platform. The company sounds similar to Brain Corp, which is crushing it with its Walmart partnership, among other initiatives. Stocked Robotics wants to be out of stealth mode by the end of the first quarter, so it is also focusing on raising funding.

Headquarters: Dresden, Germany
Industry: Robot control
Founded: 2017
Funding Status: $6.8M Series A
Reason to watch: Wandelbots wants to make it easier for everyone to program industrial robots. The system enables programming by demonstration, which isn’t new, but Wandelbots has developed a novel programming technique. Users wear smart clothing, including a jacket, that tracks human motion in real-time and converts these into robotic controls. Wandelbots said its system works with the 12 most popular industrial robotics makers. Volkswagen and Infineon are reportedly testing this in assembly and logistics.

Zivid Labs
Headquarters: Oslo, Norway
Industry: 3D machine vision
Founded: 2015
Funding: Seed
Reason to watch: A spinout from Norway’s SINTEF research lab, Zivid Labs has more than two decades of R&D expertise in optical sensors, 3D machine vision hardware and software. Its 3D color cameras target a range of applications, including de-palletizing, bin-picking, pick-and-place, assembly, packaging and quality control. Check out this case study about how 3D vision enabled a DHL e-fulfillment robot to achieve at least 400 picks per hour.

Read Source Article: The Robot Report

#AI Robotics #Startups #Researchlabs #AI

Gambling has partly made a digital shift, away from dimly-lit casinos to the brighter online world, opening up opportunities for more people to lose their money (and a few to win). What role can, and should, AI and machine learning play?

It follows that as gaming and gambling become digital activities so the use by gamblers of modern computing techniques, like artificial intelligence and machine learning, follows. While gaming companies will wish to use such technologies to minimize their losses and to maximize their profitability, gamblers will also be keen to assess how such technologies can help them to increase their chances of beating the house and coming away winning.
What is certain is that on an even-base artificial intelligence is most likely to beat a human at many cerebral pursuits. For example, in 2017 Digital Journal reported how Google's DeepMind AlphaGo artificial intelligence platform twice defeated the world's number one Go player Ke Jie.
Machines too are vulnerable. Another invention from Google - AlphaZero - defeated what was said to be the world's best chess program, Stockfish 8, after studying the complexities of chess for just four hours. In a series of contests, AlphaZero won or drew all 100 games played.
But what about the human powers of prediction and ingenuity that come into play with gambling? How does the hot-shot 'professional' gambler fare against artificial intelligence? According to Engadget advanced forms of artificial intelligence, such as Libratus system from Carnegie Mellon University, are able to match and beat human gamblers.
Commenting on the ingenuity of machine intelligence, Libratus co-developer Noam Brown said: "People think that bluffing is very human -- it turns out that's not true. A computer can learn from experience that if it has a weak hand and it bluffs, it can make more money."
From the alternative standpoint — artificial intelligence helping gamblers — Auckland University of Technology and the Nelson Marlborough Institute of Technology researchers showed how machine learning applied to sport result prediction is capable of estimating the outcome of sports with a high degree of success.

#gambling, #bluffing, #punter, #dds, #artificial intelligence

Read Source Article: digitaljournal 

Without doubt, artificial intelligence and machine learning are major areas of innovation for the greater tech community.  If you are a tech  freelancer and eager to stay in touch with future directions, you will want to know what companies like Google are investing in, the new technologies they are advancing and the research priorities they are supporting or sponsoring.

And, you are in luck!  A post yesterday by Jeff Dean, senior fellow and Google AI lead, on behalf of the Google Research Community, reviews how Google has focused its research talent and dollars.  I've provided a thumbnail summary of the priorities, quoting descriptions from the blog post.  For more comprehensive information, Dean suggests you check out publications in 2018.  Here's the list:

1. Ethical Principles And AI. “This year we published the Google AI Principles, supported with a set of responsible AI practices outlining technical recommendations for implementation. In combination they provide a framework for us to evaluate our own development of AI, and we hope that other organizations can also use these principles to help shape their own thinking..."


2. AI For Social Good. “The potential of AI to make dramatic impacts on many areas of social and societal importance is clear. One example of how AI can be applied to real-world problems is our work on flood prediction. A second example is our work on earthquake aftershock prediction. We announced the Google AI for Social Impact Challenge in collaboration with org, whereby individuals and organizations can receive grants from a total of $25M of funding, along with mentorship and advice from Google research scientists, engineers and other experts ...

3. Assistive Technology. "Much of our research centered on using ML and computer science to help our users accomplish things faster and more effectively. One example is Google Duplex, a system that requires research in natural language and dialogue understanding, speech recognition, text-to-speech, user understanding and effective UI design to all come together to enable an experience whereby a user can say "Can you book me a haircut at 4 PM today?", and a virtual agent will interact on your behalf over the telephone to handle the necessary details. Other examples include Smart Compose and Sound Search, a technology built on the Now Playing feature that enables you to discover what song is playing fast and accurately... An important focus in our research is helping to make products like the Google Assistant support more languages and allow better understanding of semantic similarity ...

4. Quantum Computing. “We have been actively pursuing research in this area, and we believe the field is on the cusp of demonstrating this capability for at least one problem (so-called quantum supremacy) which will be a watershed event for the field. We also released Cirq, an open source programming framework for quantum computers...

5. Natural Language Understanding. “Natural language research at Google had an exciting 2018, with a mix of basic research as well as product-focused collaborations. We developed improvements to our Transformer work from 2017, resulting in a new parallel-in-time version of the model called the Universal Transformer that shows strong gains across a number of natural language tasks .... We also developed BERT, the first deeply bidirectional, unsupervised language representation...

6. Perception. “Our perception research tackles the hard problems of allowing computers to understand images, sounds, music and video, and providing more powerful tools for image capture, compression, processing, creative expression, and augmented reality. In 2018, our technology improved Google Photos' ability to organize the content that users most care about ... Google Lens and the Assistant enabled users to learn about the natural worldanswer questions in real-time. In 2018, our contributions to academic research included advances in deep learning for 3D scene understanding, such as stereo magnification, synthesizing novel photorealistic views of a scene...

7. Computational Photography. "The improvements in quality and versatility of cell phone cameras over the last few years has been nothing short of remarkable... Part of this is improvements in the actual physical sensors used in phones, but (more) is due to advances in the scientific field of computational photography. This year, one of our primary efforts in computational photography research was to create a new capability called Night Sight, which enables Pixel phone cameras to 'see in the dark'...

8. Algorithms And Theory. “Algorithms are the backbone of Google systems and touch all our products ... we continued our research in algorithms and theory covering a wide range of areas. Our work in optimization spans areas from studying continuous optimization for machine learning to distributed combinatorial optimization. On the more applied side, we developed algorithmic techniques for solving set cover at scale via sketching and for solving balanced partitioning and hierarchical clustering for graphs with trillions of edges. In algorithmic choice theory, we have proposednew models and investigated the problems of reconstruction and learning a mixture of multinomial logits... Our new research include also techniques to help advertisers test incentive compatibility of ad auctions, and optimizing ad refresh for in-app advertising...

9. Software Systems. "A large part of our research on software systems continues to relate to building machine-learning models and to TensorFlow in particular ... Some of our newer research introduces a system that we call Mesh TensorFlow, which makes it easy to specify large-scale distributed computations with model parallelism, sometimes with billions of parameters. Finally, we continued our research on the security and privacy of machine learning, and our development of open source frameworks for safety and privacy in AI systems, such as CleverHans and TensorFlow Privacy...

10. AutoML.“Also known as meta-learning, is the use of machine learning to automate some aspects of machine learning... The long-term goal is to develop learning systems that can learn to take a new problem and solve it automatically, using insights and capabilities derived from other problems that have been previously solved...

11. TPUs. “TPUs are Google's internally-developed ML hardware accelerators. TPUs have enabled Google research breakthroughs such as BERT and allow researchers around the world to build on Google research via open source and to pursue new breakthroughs of their own ... the TensorFlow Research Cloud has given thousands of researchers the opportunity to benefit from even larger amounts of free Cloud TPU computing power...

12. Open Source Software And Datasets. “One of our largest efforts in this space is TensorFlow, a widely popular system for ML computations that we released in November 2015. With the launches of TensorFlow Litejs and TensorFlow Probability, the TensorFlow ecosystem grew dramatically in 2018. This year we were happy to release Google Dataset Search, a new tool for finding public datasets from all of the web...

13. Robotics. “In 2018, our goal (was) understanding how ML can teach robots how to act in the world, achieving a new milestone in the ability to teach robots to grasp novel objects and using it to learn about objects without human supervision. For the first time, we've been able to successfully train deep reinforcement learning models online on real robots, and are finding new, theoretically grounded ways, to learn stable approaches to robot control...

14. Applications Of AI To Other Fields. “In 2018, we applied ML to a wide variety of problems in the physical and biological sciences. Using ML, we can supply scientists with the equivalent of hundreds or thousands of research assistants digging through data, which then frees the scientists to become more creative and productive...

15. Health. “We have been applying ML to health ... we believe ML can make a tremendous difference. Our general approach is to collaborate with healthcare organizations to tackle basic research problems ... In 2018, we expanded our efforts across the broad space of computer-aided diagnostics to clinical task predictions as well...

16. Research Outreach. “We interact with the external research community in many different ways, including faculty engagement and student support. We host hundreds of undergraduate, M.S. and Ph.D. students as interns during the academic year, as well as providing multi-year Ph.D. fellowships. The Google AI Residency, (is) a way to spend a year working alongside and being mentored by researchers at Google. Each year, we also support faculty members and students on research projects through our Google Faculty Research Awards program...

17. New Places, New Faces. “In 2018, we welcomed many new people into our research organization. We announced our first AI research office in Africa, located in Accra, Ghana. We expanded our AI research presence in ParisTokyo and Amsterdam, and opened a research lab in Princeton. We continue to hire talented people into our offices all over the world, and you can learn more about joining our research efforts here..."

Jon Younger authored Agile TalentHR From the Outside In and HR Transformation, among other books and articles in several publications and his blog “Freelance Revolution.”

Read Source Article: Forbes

#AI #MachineLearning #NaturalLanguage #deepLearning #Robotics #DataScience

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