Convolutional Neural Networks or CNNs are increasingly favored in machine learning development programs associated with clinical research and surgical science. In the latest report, the advanced CNNs have been linked to better clinical outcomes among patients inflicted with different types of hip fractures. Overall, these CNNs are favored over human-based clinical reports because of a higher level of accuracy in hip fracture classification, better patient experience, and reduced ortho care costs.

According to a published report, a majority of hip fractures are managed using surgical procedures. These are aimed at restoring pre-fracture function and relieving pain. Surgical procedures are costly in nature, and wrongly classifying a hip fracture could increase the risks of morbidity as well as mortality for elderly and at-risk patients. Artificial Intelligence (AI) could reduce these risks significantly, especially in advanced hip bone fractures where human clinicians may have to take a decisive call on going ahead with a surgical procedure to restore full function and mobility. That’s where machine learning algorithms using Convolutional Neural Networks are gaining popularity in the world. A new machine learning process designed to identify and classify hip fractures has been shown to outperform human clinicians.

Two convolutional neural networks (CNNs) developed at the University of Bath were able to identify and classify hip fractures from X-rays with a 19% greater degree of accuracy and confidence than hospital-based clinicians, in results published this week in Nature Scientific Reports.

The research team, from Bath’s Centre for Therapeutic Innovation and Institute for Mathematical Innovation, as well as colleagues from the Royal United Hospitals Trust Bath, North Bristol NHS Trust, and Bristol Medical School, set about creating the new process to help clinicians make hip fracture care more efficient and to support better patient outcomes.

They used a total of 3,659 hip X-rays, classified by at least two experts, to train and test the neural networks, which achieved an overall accuracy of 92%, and 19% greater accuracy than hospital-based clinicians.

Convolutional Neural Networks and Their Involvement in Hip Fracture Classification for Effective Treatment Is Crucial in Managing High Costs

Hip fractures are a major cause of morbidity and mortality in the elderly, incurring high costs to health and social care. Classifying a fracture prior to surgery is crucial to help surgeons select the right interventions to treat the fracture and restore mobility and improve patient outcomes.

The ability to swiftly, accurately, and reliably classify a fracture is key: delays to surgery of more than 48 hours can increase the risk of adverse outcomes and mortality.

What are the different types of hip fractures?

Fractures are divided into three classes – intracapsular, trochanteric, or subtrochanteric – depending on the part of the joint they occur in. Some treatments, which are determined by the fracture classification, can cost up to 4.5 times as much as others.

In 2019, 67,671 hip fractures were reported to the UK National Hip Fracture Database, and given projections for population ageing over the coming decades, the number of hip fractures is predicted to increase globally, particularly in Asia. Across the world, an estimated 1.6 million hip fractures occur annually with substantial economic burden – approximately $6 billion per year in the US and about £2 billion in the UK.

As important are longer-term patient outcomes: people who sustain a hip fracture have in the following year twice the age-specific mortality of the general population. So, the team says, the development of strategies to improve hip fracture management and their impact of morbidity, mortality and healthcare provision costs is a high priority.

Rising Demand of CNNs in Radiology Departments

One critical issue affecting the use of diagnostic imaging is the mismatch between demand and resource: for example, in the UK the number of radiographs (including X-rays) performed annually has increased by 25% from 1996 to 2014. Rising demand on radiology departments often means they cannot report results in a timely manner.

Prof Richie Gill, lead author of the paper and Co-Director of the Center for Therapeutic Innovation, says: “Machine learning methods and neural networks offer a new and powerful approach to automate diagnostics and outcome prediction, so this new technique we’ve shared has great potential. Despite fracture classification so strongly determining surgical treatment and hence patient outcomes, there is currently no standardized process as to who determines this classification in the UK – whether this is done by orthopedic surgeons or radiologists specializing in musculoskeletal disorders.

“The process we’ve developed could help standardize that process, achieve greater accuracy, speed up diagnosis and alleviate the bottleneck of 300,000 radiographs that remain unreported in the UK for over 30 days.”

Mr Otto Von Arx, Consultant Orthopaedic Spinal Surgeon at Royal United Hospitals Bath NHS Trust, and one of the paper co-authors, adds: “‘As trauma clinicians, we constantly strive to deliver excellence of care to our patients and the healthcare community underpinned by accurate diagnosis and cost-effective medicine.

“This excellent study has provided us with an additional tool to refine our diagnostic armamentarium to provide the best care for our patients. This study demonstrates the excellent value of collaboration by the RUH and the research leader, the University of Bath.”

The study was funded by Arthropplasty for Arthiritis Charity. The NVIDIA Corporation provided the Titan X GPU that carried out machine learning functions, through its academic grant scheme.

The University of Bath is one of the UK’s leading universities both in terms of research and our reputation for excellence in teaching, learning and graduate prospects.

The University is rated Gold in the Teaching Excellence Framework (TEF), the Government’s assessment of teaching quality in universities, meaning its teaching is of the highest quality in the UK.

In the Research Excellence Framework (REF) 2014 research assessment 87 percent of our research was defined as ‘world-leading’ or ‘internationally excellent’. From developing fuel-efficient cars of the future to identifying infectious diseases more quickly, or working to improve the lives of female farmers in West Africa, research from Bath is making a difference around the world.

Source: The article Machine learning outperforms clinical experts in the classification of hip fractures is available at https://www.nature.com/articles/s41598-022-06018-9

Murphy, E.A., Ehrhardt, B., Gregson, C.L. et al. Machine learning outperforms clinical experts in the classification of hip fractures. Sci Rep 12, 2058 (2022).

The post Convolutional Neural Networks Support Better Patient Outcomes in Hip Fracture Cases appeared first on AiThority.

Explainable Machine Learning is a sub-field within Data Science and Artificial Intelligence (AI). It is also referred to as X-ML or XML, and projected to be the next biggest avenue for all AI and machine learning applications in the future. Abacus.ai, a leading AI startup has made substantial progress in the field of Explainable Machine Learning, which has been published in its latest paper. This paper is all set to appear at Neural Information Processing Systems (NeurIPS) Conference 2021, to be held between 7-10 December later this year.

Explainable Machine Learning or XML is tested based on three key parameters – transparency, interpretability, and explainability. For any plain machine learning model to qualify as an XML algorithm, it should be understood using concepts of human-level intelligence. In recent years, significant developments have been made in this area with an aim to bring AI and Deep Learning models out of the conventional “black box’ domains. As per IBM, machine learning models are often thought to be behaving as black-boxes that are hard to interpret.

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In its latest paper on XML, Abacus.ai has released the workflow associated with XAI-BENCH. XAI-BENCH is a battery of synthetic datasets for “benchmarking popular feature attribution algorithms.” The synthetic dataset could be configured and re-engineered to simulate real-world data using popular explainability techniques across several evaluation metrics.

AI is becoming advanced and human brains behind this trend associate the evolution to powerful XML techniques which are entrusted to bring computing out of black-box approaches. The black box legacy within conventional AI ML algorithms is so deeply entrenched that it would require much more than publishing papers on XML. Abacus.ai is putting its brain and brawn behind XML models to help scientists and AI engineers understand the various ways they can create an algorithm that humans can understand and evaluate what’s happening inside the ‘black-box’ of the AI ML field.

Role of Explainable Machine Learning in Modern Data Science

Explainable Machine Learning or XML is already influencing the penetration of advanced AI in various industries. Some of the key applications of XML in the modern era have been listed below:

In healthcare and telemedicine: XML is used to optimize image analysis, diagnostics, and decision-making for patient management processes;

In banking and loan approval systems, where XML is used to evaluate credit health and financial fraud risks;

In blockchain and crypto, where XAI  and machine learning algorithms can be used to fully secure and decentralize the “highly sensitive system for storing and processing AI-generated data”, and so much more…

As we continue to trace the next phase of advanced AI growth in the marketplace, it is expected that companies like Abacus.ai would emerge as the top contributors of trustworthy AI abilities that break the conventional mold of black-box modeling.

[To share your insights with us, please write to sghosh@martechseries.com]

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Since the beginning of the internet, digital marketers, specifically SEOers, have been chasing Google’s algorithm with every change made based upon the latest algorithm update while forgetting that Google is in fact catering to humans.

Humans are the core of what we do as marketers, and as such, they should be at the center of every strategy and action. When it comes to planning your site structure, the same should be applied.

WHAT IS SITE STRUCTURE?

Site structure is the way you group, link and present the content, services and products of your site to the users. In summary, it would be how you organize your website’s content. The site structure can sometimes also be referred to as a taxonomy within the website.

Practicality and experience should be balanced in order to achieve a visually appealing site and an organized structure that is intuitive to the customer.

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WHY SHOULD YOU CARE?

There are two main reasons why brands should care about site structure. These are:

Good Housekeeping: Just like an organized closet that makes it much easier to find any garment of clothing, a well-structured site will help your business keep a clean website with no duplicated content, minimal 404 pages and a seamless user experience.

Prioritization of Content: Google will better understand the prioritization of the content as it will be structured based on importance, helping with better rankings and optimized crawling.

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HOW DOES SITE STRUCTURE AFFECT SEO?

Google’s objective is to provide users with the most useful, accurate and accessible results for their searches, so much so that Google has rolled out a series of new ranking signals based on its core web vitals that refer directly to user experience. This means we also need to understand the importance of having humans and human behavior at the core of the site structure planning.

But there are many other areas in which an optimized site structure will help us improve our visibility:

Site Crawlability: you will also help Google making the crawls on your site more efficient, getting many benefits from it such as:

• Getting crawled more often

• Getting most of your pages, if not all, crawled at once

Indexability: an optimized site structure will allow robots to crawl your pages, increasing the chances of having all of them indexed faster

Cannibalization: thanks to the site being properly structured you’ll be able to give Google an indication of the priority of the pages of your site, as well as point out which pages are secondary or subpages of the main topic

Duplicated content: when the site is structured, the content follows a path, which reduces the chances of creating or publishing the same content more than once

Internal linking & link authority: An optimized internal linking strategy will ensure a healthy link flow passing through all pages, from the home page to the latest created page

HOW DOES AN OPTIMISED SITE STRUCTURE HELP USERS?

Using Neuroscience principles, we can improve user experience, as this is key to understanding how the human brain works and how our site can have an impact on it. The main principles to have in mind when building the site structure, and our site in general, are:

Subconscious and the first impression

The subconscious mind is the most powerful as it notices things faster than our consciousness, it is also responsible for our emotions

The subconscious can judge a site in milliseconds; hence the importance of design and the impact this has, as if a website feels right, people will trust it.

First impression is critical, if the site doesn’t feel trustworthy and reliable, users are unlikely to come back

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Simplicity

Humans nowadays have stopped reading content and tend to scan through headlines, which forces marketers to ensure it is properly structured and highlighted so they can reach the destination faster

Logic

It is extremely important that your site flows properly, arranging the elements to create a natural dialogue will enhance user experience

Avoid questions that may pop in the users’ heads while navigating your site and menu: “What’s this?” “Where should I find x products?” “How did I get here?”

In summary, our brain requires a certain order and structure to make sense of the content presented in front of us. The way it is presented can and should make us attracted to the brand whilst the content should engage and take us on the website’s intended path.

Our main goal is for the users (and crawlers) to find the solution to their problems fast and seamlessly. Although, following these principles will also help us convert those users into loyal customers that can engage and bond with the site, coming back again and again.

MORE WAYS OF HELPING USERS NAVIGATE YOUR SITE

1. Welcoming users through the homepage

Your home page, the place where you welcome the users, the nucleus of your site and usually, the page with the highest traffic and incoming links, makes it the perfect starting point to link to your most important pages.

Humans like order, simplicity and logic, so we should follow this pattern to create and link from our homepage.

How can we create a homepage that is catered for humans, yet SEO and crawler-friendly?

Just follow these simple steps:

• It should take no more than 5 seconds to identify what the page is about, so be sure this is clear at the top of your homepage

• It should ease the user down the intended path to purchase, by proving the right content/product/service recommendations, not necessarily straight to a conversion page

• Call to actions should be clear and stand out from the rest of the content. We are the ones guiding the user through our content, which can be done via CTAs

• Most important links/ categories/ products should be placed here

2. The navigation menu: the user’s guide to your site

The menu is key for users to understand the structure of the site. When structuring it we should ensure that it follows logic and leaves no questions needing to be asked, as the Neuroscience principles have taught us. The goal is to ease the path for users to find their answers as quickly as possible, avoiding unnecessary complications.

Sephora Makeup navigation is a great example of this as it clearly defines and categorizes the products based on body areas, which makes finding products easy for both makeup experts and beginners:

3. Helping users find their way with breadcrumb trails

Just like maps in shopping centres that show you where you are and guide you to where you want to go, these clickable paths are usually added to the desktop version of the site. It helps users go back to different related pages as well as understand where they are within your site structure. It also helps crawlers to understand where on the site this page is located as well as its priority and relationship with other pages.

In summary, we can assure that order and structure are key to start planning your human-friendly site structure and that having humans at the core of your strategy will not only increase current visitors and conversions, but it could potentially have a positive effect on the lifetime value of the customers as well.

[To share your insights with us, please write to sghosh@martechseries.com]

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Ambow Education Holding Ltd. (“Ambow” or “the Company”) China’s leading provider of educational and career enhancement services, announced it has entered into a cooperation agreement (“Cooperation Agreement”) with Kylinsoft, a leading Chinese software developer. Pursuant to the Agreement, Ambow and Kylinsoft will join forces to enhance the cyberspace talents training system by designing training classes, offering career certification service, internship placement, employment service and co-founding industrial colleges, in a bid to foster an ecosystem of technologically advanced talent to meet the practical needs of national cyberspace development and ultimately boost the overall development of the Internet.

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As China accelerates efforts to improve infrastructure, the demand for IT innovation talent in the nation is steadily rising. As a pivotal software developer for the national plan, Kylinsoft takes the lead or participates in development, and drafts more than thirty technology standards spanning country-level, industry-level and alliance-level, building strong technology capabilities in cyberspace. In recent years, Ambow has been focusing on career certification service offerings and supporting the new engineering talents development. Therefore, leveraging Ambow’s ability to deliver mature education solutions for high schools and enterprises while building a talent ecosystem and offering comprehensive services, both parties will work together to provide a suite of career-oriented education services across a series of Kylinsoft operating system products. The services will cover training courses, internship placement and job recommendations for domestic colleges and undergraduates, as well as assistance in developing and promoting various skill certification and training projects. With a well-versed training system, the two parties are committed to cultivating a group of management experts and professionals who have deep understanding and extensive experience in related technologies across the cyberspace value chain, so as to help meet talent needs for the nation’s cyberspace development, and propel rapid growth of the sector.

Dr. Jin Huang, President and Chief Executive Officer of Ambow, commented, “This strategic cooperation marks an important step toward advancing our nation’s homegrown cyberspace talent scheme. It is also an innovative, practical solution. As we move forward, we will combine our rich experience and advanced suite of solutions, products and services in career education and training space with Kylinsoft and its leading operating system technology capabilities to provide a comprehensive suite of talent training services across Kylinsoft series. We are committed to making a concerted effort to promote cyberspace talent growth initiatives and initiate a new chapter of talent cultivation in the sector.”

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Cities across the United States are underreporting their carbon emissions by an average of 18.3% according to Nature Communications, the journal for research across the natural sciences.

It has reported huge discrepancies in measurement, with some cities under-reporting emissions by as much as 145.5%, and the total amount of potentially unreported carbon equating to 129 million metric tons.

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But Kevin Gurney of Northern Arizona University stressed there is no reason to suggest that cities are deliberately under-reporting. It is much more likely that city governments and officials simply did not have the technology to measure emission output accurately – until now.

Gurney and his team have developed ‘Vulcan’, an automated measurement system that can estimate fossil-fuel emissions at specific geographic points and across large areas. The team compared Vulcan’s estimates of greenhouse gas emissions between 2010 and 2015 with those reported in 48 city inventories, which discovered many US cities were not measuring their emissions accurately.

Gurney used measuring heating emissions as an example: “Heating oil statistics are difficult to get. Cities will often just not include the heating oil in their total building estimates.”

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He added that cities are also using different methods to measure emissions from various sources, such as airborne, on-road, and marine:

“The analysis highlights the need for a systematic, consistent approach to accounting for carbon emissions across the US, because inaccurate estimates make it difficult to assess how effective emissions reduction efforts are.”

A recent article from the World Economic Forum, says cities are crucial for the journey to net-zero emissions. Despite only covering 3% of the Earth’s land surface, urban areas are responsible for more than 70% of global carbon emissions.

Emily Tan, City Solutions General Manager at Shell Renewable and Climate Solutions, believes there has never been a more important time for integrated solutions:

“Integrated solutions need to be innovated and delivered. This will require unprecedented collaboration between the government, industry, and society. But the urgency has never been greater. After all, making cities sustainable places to live and work for future generations will be imperative if the world is to meet the broader goals of the Paris Agreement and move closer to a net-zero emissions world.”

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Cityzenith CEO Michael Jansen is adamant that the solution to the effective measurement and management of carbon emissions within urban areas is Digital Twin technology.

“This report should be a real eye-opener to everyone working to address the 2016 Paris Agreement and push back against Climate Change.

“We already knew that urban emissions were by far the biggest contributor to the greenhouse gases causing our world to heat up and threaten human life and prosperity, but it now seems that some of the parameters must change through this inadvertent under-reporting.

“Fortunately, Digital Twin 3D modelling through a powerful platform like our SmartWorldOS software can aggregate a****** data and use AI to develop lasting solutions to the problems highlighted by Nature Communications and the Vulcan team.

“We are already working with major cities across the world and also donating SmartWorldOS one at a time to some as part of our ‘Clean Cities – Clean Future’ initiative.”

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With Azure Virtual Machines or VMs, users can successfully create a high-performance Scale-Out/Scale-In silicon design strategy. These help DevOps teams to achieve significant run-time speedup and cost optimization.

Intel’s Deep Learning Boost Technology (Intel DL Boost) is now the central feature of new general purpose and memory-optimized Azure Virtual Machines (VM). Microsoft has announced that the new Azure VMs are based on the second generation Xenon Platinum Cascade Lake (8272 CL) configuration, capable of running at 2.5 GHz and all-core turbo frequency of 3.4 GHz. Powered by Intel DL Boost, the Azure VMs also features other top-end technologies, including the Intel® Advanced Vector Extensions 512 (Intel® AVX-512), Intel® Turbo Boost Technology 2.0, and Intel® Hyper-Threading Technology.

For users working with the v3 VMs, switching to v4 sizes will deliver a better price-per-core performance option.

What is Intel Deep Learning Boost (DL Boost) Technology?

Intel DL Boost Technology is a scalable embedded AI performance enhancer for complex IT workloads. It is fitted into the Intel Xeon Scalable processors to extend inference performance for Deep Learning workloads to handle the Vector Neural Network Instructions extension (VNNI). These VNNI AVX 512 are used in new-age AI applications such as Voice/Speech recognition, Object Classification, Language Translation and Image Processing, and much more.

With Intel’s DL Boost Technology at its core, Azure VMs will run a totally new line of families –

• Azure Ddv4 and Ddsv4 and Edv4 and Edsv4; (general availability)
• Azure Dv4 and Dsv4 and Ev4 and Esv4 (only preview).

These VMs rely on remote disks without expanding on temporary local storage, impacting performance by up to 20% of local CPU usage compared to all previous versions of Azure VM families.

Specific Features of Azure VMs Running on Intel DL Boost

The new DDv4 and Ddsv4, and Edv4 and Edsv4 have a much larger SSD storage designed to amplify benefits from low latency and temporary storage requirements. High-speed local storage enable IT teams to manage caches or temporary files better and faster.

The previous generation of Azure VMs includes AV-series, B-series, DCv2-series and so on. These were deployed to protect various operations in CPU and GPU configuration, data management and coding facility, in addition to enhancing value proposition of the various general-usage workloads.

Apart from greater local storage, these VMs are also capable of offering better local disk IOPS for both Read and Write operations.

New VMs from Azure powered by the Intel Boost technology can balance memory-to-CPU performance up to 2400 GiB and 64 vCPUs. According to Microsoft Azure, these scenarios are ideal for development and testing, small to medium databases, and low-to-medium traffic web servers.

On the other hand, the Edv4 series includes 504 GiB of RAM, and also include local SSD storage (up to 2,400 GiB) meant for the operational management of the RDBs and in-memory analytics.

At the time of this announcement, Intel’s Jason Grebe, CVP Cloud and Enterprise, explained the deeper nuances of working with Azure VMs. Jason said, “The launch of Azure D-v4 and E-v4-series virtual machines further extends the Microsoft IaaS portfolio to meet the diverse needs of our customers. Powered by 2nd Generation Intel® Xeon Scalable Processors, these virtual machines offer optimized application performance for web and data services, desktop virtualization and business applications moving to Azure.”

(To share your insights or feature your company updates, please write to us at sghosh@martechseries.com)

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Tell us about your interaction with smart technologies such as AI and NLP platforms. 

Neurala is a Boston-based company building Artificial Intelligence emulating brain function in software. Neurala commercializes a new Deep Learning technology called Lifelong-DNN. Neurala’s AI is the only one in the market that gets smarter after every use, instantly and continuously learning new information in any computing substrate, from servers to small devices.

Between myself and Neurala’s other co-founders, Dr. Heather Ames Versace and Dr. Anatoli Gorchet, we have four PhDs, two dozens patents, and decades of experience working together to create groundbreaking AI.

Tell us more about Neurala and how your customers benefit from leveraging it.

Neurala develops Deep Learning neural network software to make devices more intelligent, engaging and useful. Our Brain Builder platform accelerates and streamlines the creation, deployment, analysis, and management of deep learning applications. For customers, this means getting AI from conception to deployment in minutes, with a lower barrier to entry for enterprise AI deployment, ultimately, making it less expensive and time-consuming to develop AI solutions. Brain Builder leverages Neurala’s patented Lifelong-DNN technology to gain incremental knowledge and makes complex AI deployments accessible to non-experts.

Neurala’s award-winning AI is based on technology originally developed for NASA, DARPA and the U.S. Air Force and is now deployed commercially on over 25 million products like drones, mobile phones, cameras, and other smart devices.

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How did you start in this space? What galvanized you to start Neurala?

Myself and my Co-founders, Dr. Heather Ames Versace and Dr. Anatoli Gorchet, all met while we were working on our PhDs at Boston University. AI was still in its infancy, but we had a vision for where it would go in the future. Specifically, we had the idea of being able to run AI systems on mobile devices, which was impossible at that time due to computing power and scalability.

But, we wrote a patent for the technology and started off working on high-level projects for governmental agencies like DARPA and NASA, most of which were pretty ambitious. And after realizing that there was significant commercial interest in this kind of technology, we decided to take the company out of the lab in 2013 when we joined TechStars. It was an exciting time because we knew we were ahead of the market.

Tell us more about your role in NASA’s Mars Rover project!

Neurala’s story starts with NASA. When my co-founders and I were still at Boston University, we learned of NASA’s goal to enable autonomy on the Mars Rover. NASA needed AI that would allow the Rover to learn, perceive, and navigate autonomously on Mars, on a small compute-power envelope, without a connection to Earth. We immediately started working on trying to solve this challenge, which was something that the AI industry as a whole had yet to achieve. We had to make our AI 1000x more efficient and more portable than ever before. Ultimately, we designed an AI “brain” that was able to understand its environment without any connection to Earth, uniquely relying on edge processing, so the Mars Rover would be able to navigate its surroundings without human direction.

How do you differentiate between technologies for AI and Machine Learning? Who are you competing within this AI software landscape?

When we started, there were very few pioneering AI companies – now, everyone is claiming they are powered by AI. It’s critical to be skeptical, pay attention to whether so-called “experts” have published peer-reviewed papers, and to be honest about what AI can and can’t achieve. What we’re working on right now has never truly been done before, so our direct competitors are limited. Google and Microsoft come closest, but we still maintain a competitive advantage and can move very fast. 

What are the biggest challenges and opportunities for businesses in leveraging technology to optimize AI outcomes? 

AI enables businesses to be more efficient internally while also curating engaging and targeted end-user experiences. That said, we live in a world that is constantly changing and evolving, so the challenge is creating AI solutions that can do the same. Devices like drones, robots, cameras and other technologies that are incorporated into business processes need to be able to assess and adapt to any problems and situations they encounter in real-time, as they arise.

Humans are so good at doing this on a daily basis, that we forget how our “AI” continuously adapts to our changing world. One of the biggest challenges for enterprises is understanding that their AI needs to also evolve, daily, as real-life AI applications live in the same world where we live: one that changes all the time.

Traditional approaches to deep learning make continuous learning impossible. That’s why Neurala has developed L-DNN technology that allows AI to learn quickly, at the edge/on-device, and continuously over time. This means that rather than having to retrain AI systems for new use cases or situations that are unfamiliar, AI is able to keep up. In turn, AI can be more useful to their human counterparts – ultimately performing at the expectation that businesses have when it comes to how AI can improve how we work and businesses’ bottom lines.

How should young technology professionals train themselves to work better with Automation and AI-based tools? 

Even though AI has matured and is gaining traction in a number of areas, there’s still a significant skills gap when it comes to sourcing AI talent that can keep up with demands for the technology. In fact, some estimate that there are only 300,000 AI engineers worldwide, but millions are needed to keep up with advancements in the field and deliver on the technology’s promise.

For young tech professionals preparing to enter the workforce, it’s important to look for tools to help simplify the process – especially when you consider that many young people may not have the time or resources to get a Ph.D. in AI, and will need to find a way to bridge the gap in education. That’s part of the inspiration behind Neurala’s Brain Builder platform. As mentioned, Brain Builder seeks to lower the barrier to entry when it comes to AI development.

It makes AI more accessible for developers and organizations by providing an easy-to-use platform to manage and annotate training data. We like to think of Brain Builder as the “WordPress for Brains” – recalling how WordPress (and its copycats) made web development easy for non-experts. The world is ready for the same revolution in AI, and Neurala will lead this next step.

In fact, we recently partnered with academic institutions including Boston University, and the Chinese Software Developer Network (CSDN), to provide education programs, and access to Brain Builder. The goal is to equip developers with the tools and knowledge they need to build enterprise-grade AI for the real world. We’re passionate about investing in the future of AI, which begins with fostering the next generation of AI talent.

For up-and-coming AI talent, my advice would be to seek out specialized courses and curriculum that provide you with actual tools, in addition to coursework, if you want to ensure you’re ready to solve real-world AI challenges.

As a tech leader, what industries do you think would be fastest to adopting Analytics and AI/ML with smooth efficiency? What are the new emerging markets for these technology markets?

There are several industries where AI adoption can help ease the workload and improve efficiency. From drones and robotics to manufacturing and inspections, AI can be integrated into existing workflows to speed up processes that would traditionally be meticulous and time consuming for humans. But it doesn’t stop there – any industry where there is a vision-based AI challenge, with a lot of repetition, is a prime candidate for AI intervention. When implemented correctly, AI can alleviate resource challenges and inefficiencies, allowing employees to focus only on the information that’s important to them.

How do you see the raging trend of including involving AI and Machine learning in a modern CIO/CMO’s stack budget? 

We have seen AI cross the threshold from proof of concept to technology that can be deployed at scale. CIOs, CMOs, and other key decision-makers have certainly taken notice. That said, there is still a lot to learn, and organizations need to make sure they’re fully prepared before they embark on an AI project.

Specifically, organizations should focus on using AI to solve one key problem for a specific use case, versus trying to be the end-all, be-all solution that will solve all problems.

AI can significantly improve the way people do their jobs in a range of settings, so companies shouldn’t hold out for technical advancements that will replace their entire workforce. Instead, companies should devise use cases that can make an immediate impact on efficiency and workflow, and test those use cases first. And, if robots are built with L-DNN technology, they’ll be well-poised to adapt to new use cases and learn iteratively over time.

What is the data engine behind Brain Builder? What data architecture drives your success with Brain Builder? 

Brain Builder is powered by our Lifelong-DNN technology within our proprietary data architecture. L-DNN pushes AI beyond inference and allows it to learn continuously, either on a server or on a power-constrained device, significantly reducing data needed, training time and enabling real-time learning and improvement of the AI. When deployed on-device, L-DNN sidesteps the need to store or ship customer data outside the device for learning on servers, presenting as the only technology in the market that can seriously address the issue of data confidentiality and privacy, thanks to the ability to learn on a small computer footprint.

What is the biggest challenge to Digital Transformation with AI in 2019? How does Neurala contribute to a successful Digital Transformation? 

As enterprises look to undergo a Digital Transformation, AI is often top of mind. That said, there are a number of misconceptions amongst organizations in terms of what to expect when it comes to AI adoption. There is often a lack of IT infrastructure and a shortage of AI experts to guide the transition, presenting multiple barriers to those seeking to integrate AI into their businesses.

Neurala can help companies looking to tackle Digital Transformation with AI through our Brain Builder platform. Making AI development easier and more accessible, Brain Builder enables more and more organizations to take on AI adoption. Rather than being dependent to external “AI contractors”, enterprises can take control of their AI strategy with internal resources using the right tools, and as a result, product managers, engineers, and their organizations will be able to test and build AI solutions faster, deploy them, and grow them over the AI lifecycle.

How potent is the Human-Machine intelligence for businesses and society? Who owns Machine Learning results? 

Once upon a time, workers had to type on a typewriter. Then came the PC, and humans ‘melded’ their skillset with their digital cousin. AI will be as a meaningful and powerful to transformation as the PC was, adding a third player to the equation: a human, a machine, and a mind for the machine. Humans don’t need to provide all the thinking for the machine, freeing up their precious minds for other tasks, and augmenting the throughput of a worker by orders of magnitude.

Where do you see AI/Machine Learning and other smart technologies heading beyond 2020?

Rather than humans bending their privacy to enable AI, the latter will need to technologically bend and evolve to fit our privacy needs. New headlines about AI and privacy are popping up each and every day. Today’s technical requirements for AI simply won’t cut it in light of current privacy standards, nor will the demands for usability and adaptability of AI in deployment.

As a result, we will see lifelong learning deep neural networks (L-DNN) emerge as the new paradigm for AI learning. This will enable lifelong learning at the edge and directly on devices, eliminating the need to store all of the training data. This new approach will be the only way to build solutions that are both useful and respectful of people’s privacy.

The Good, Bad and Ugly about AI that you have heard or predict – 

2018 saw the continuation of contentious debates around technology development across national borders. In the case of AI specifically, many have advocated for the U.S. to take a competitive approach with other nations, avoiding collaborations or knowledge-sharing in light of politics, trade restrictions, and privacy concerns.

While privacy and ethics should be central to any discussion about AI – whether domestically or abroad – the industry needs to shift its mindset toward creating a global economy around AI. AI has the potential to help solve some of the world’s most pressing challenges today and benefit society in a number of ways, but only if we open the door to conversations between the many nations leading the AI revolution.

Like any technology, AI is a tool whose applications reflect the intentions of its creator. It is up to us, as a global AI economy, to show the path, and come together to guide AI use cases in the right direction. Without a global approach, AI will fail to serve all of the communities and people that technology has the potential to reach.

What is your opinion on “Weaponization of AI and Automation”? How do you promote your ideas? 

I’m a strong believer in restricting the weaponization of AI. AI is often spoken of as a potential force for evil, but that isn’t how it has to be. I joined a movement with the goal of stopping the weaponization of AI and signed an open letter to the UN aimed at sensitizing on the issue of weaponization of AI and Robotics.   

What technologies within AI/NLP and Deep Learning are you interested in? 

We are interested in technological developments in the area of continual learning. 

What’s your smartest work-related shortcut or productivity hack? 

I’m Italian, so it has to be an espresso.  

Tag the one person in the industry whose answers to these questions you would love to know.

Heather Ames

Thank you, Max! That was fun and hope to see you back on AiThority soon.

• About Massimiliano
• About Neurala

About Massimiliano

Dr. Massimilliano Versace is the CEO and Co-Founder of Neurala. Max continues to lead the world of intelligent devices after his pioneering breakthroughs in brain-inspired computing.

He has spoken at numerous events including a keynote at Mobile World Congress Drone Summit, TedX, NASA, the Pentagon, GTC, InterDrone, GE, Air Force Research Labs, HP, iRobot, Samsung, LG, Qualcomm, Ericsson, BAE Systems, AI World, ABB, and Accenture among many others. His work has been featured in TIME, IEEE Spectrum, CNN, MSNBC, The Boston Globe, The Chicago Tribune, Fortune, TechCrunch, VentureBeat, Nasdaq, Associated Press and hundreds more. He holds several patents and two PhDs: Cognitive and Neural Systems, Boston University; Experimental Psychology, University of Trieste, Italy.

About Neurala

Neurala developed The Neurala Brain—deep learning neural network software that makes devices and products like drones, mobile phones and cameras more intelligent, engaging and useful. Neurala provides customized solutions ranging from high-end applications to inexpensive everyday products.

With The Neurala Brain and an ordinary camera, products can learn people and objects, recognize them in a video stream, find them in the video, and track them as they move. The Neurala Brain is based on technology originally developed for NASA, DARPA and the US Air Force and is now deployed commercially on over a million smart devices

The post AiThority Interview with Massimiliano Versace, CEO and Co-Founder at Neurala appeared first on AiThority.

AI-powered mineral deposit exploration platform, EARTH AI has announced $2.5 million funding from Gagarin Capital, the VC firm specializing in AI, and Y Combinator. With this funding, the AI start-up has to be recognized as one of the fastest-growing mineral exploration companies in the world. With its AI technology, miners and geology companies can predict the location of new ore bodies far cheaper, faster, and with more precision than previous methods.

Previously, EARTH AI raised $1.7 million in two seed rounds from AirTree Ventures and Blackbird Ventures and high-net-worth angel investors. The new round will help the company continue to pursue its mission of fundamentally improving the efficiency of mineral exploration with the help of cutting-edge technology.

The Brain Behind EARTH AI’s Data Science

EARTH AI is a full-stack mineral exploration and mining company, it has built a proprietary mineral targeting technology able to predict the location of new mineral deposits 50X better than the traditional exploration methods. The company has vertically field operations throughout all stages of mineral exploration, from geological mapping to drone-based geophysical surveying and exploration drilling.

Read More: StrongKey Expands Market Share with Tellaro Line of Data Security Appliances

In their official catalog, EARTH AI explains how they train Machine Learning (ML) algorithm with data from all over the world.

It states —

“We process various satellite datasets from NASA and feed them into our pre-trained ML models. Innovative AI solutions can be generated anywhere in the world where a mineral is likely to exist. We then use the ML outputs, such as probability maps and cluster maps, to guide our field work targets. Based on field work results, such as PXRF and geochem, we can re-enforce the ML models in an iterative process.”

More specifically, EARTH AI’s technology uses Machine Learning techniques on global data, including remote sensing, radiometry, geophysical and geochemical datasets, to learn the data signatures related to industrial metal deposits (from gold, copper, and lead to rare earth elements), train a neural network, and predict where high-value mineral prospects will be. EARTH AI can discover, stake, and drill-test brand new mining regions—a crucial task for growing industrial development worldwide. Demand for these minerals continues to grow rapidly from the technology sector, yet existing mines are becoming depleted and will be unable to secure the future supply.

EARTH AI first drill program made history, confirming the world’s first mineral deposit discovered by AI. It contains Lead and Vanadium, the latter is used in building Vanadium Redox Batteries that increasingly used for bulk energy storage for large industrial applications. The company has secured exclusive mineral rights for 18 prospective sites, predicted by its technology.

AI Accelerates Mineral Exploration 50X

EARTH AI founder and CEO Roman Teslyuk said, “EARTH AI has huge ambitions, and this funding round will supercharge us towards reaching our milestones. Gagarin has built and supported a strong community of AI experts and top-in-class engineers, and we’re excited to join that community.”

In 2018, EARTH AI field-tested remote unexplored areas and ended up with ground-breaking 50X better success rate than traditional exploration methods, while spending on average $11,000 per prospect discovery; in Australia, companies often spend several m************** to arrive at the same result.

“EARTH AI is taking a novel approach to a large and important industry — and that approach is already showing tremendous promise”, Mikhail Taver, Partner at Gagarin Capital said.

Mikhail added, “We believe EARTH AI is on track to become the world’s largest mineral exploration company, discovering and partially owning the majority of new metal mines, and are excited to contribute the funds and AI expertise needed to make that a reality.”

Jared Friedman, Y Combinator partner, said, “At YC, we are particularly excited by new applications of artificial intelligence. The possibility of discovering new mineral deposits with AI is a fascinating and thought-provoking idea. Earth AI has the potential not just to become an incredibly profitable company, but to reduce the cost of the metals we need to build our civilization, and that has huge implications for the world?”

EARTH AI was founded by Roman Tesyluk, a geoscientist with eight years of mineral exploration and academic experience. Prior to starting EARTH AI, he was a Ph.D. Candidate at The University of Sydney, Australia and obtained a Master’s degree in Geology from Ivan Franko University, Ukraine. A member of the Australian Institute of Mining and Metallurgy, he won the Ukrainian Geology Olympiad in 2013 and was the first person in Australia to receive Entrepreneur Visa 188E.

Currently, EARTH AI is conducting fieldwork in remote greenfields areas of Australia working alongside the AI system to find new mineral prospects. It had also developed a UAV-based magnetic data acquisition system. It formed a fully certified 2 drone pilot team to conduct high-precision magnetic surveys on our prospects.

Its main competitors are:

• KoBold Metals
• GoldSpot Discoveries

Read More: SMBC Selects Exiger’s DDIQ as a New AI AML Tool in EMEA Region

The post EARTH AI Scripts New Chapter in AI-Based Mineral Exploration appeared first on AiThority.

Today, we dive into the most cutting-edge component of BI tech that data analysts are eyeing closely in 2019 — Data Storytelling

Business Intelligence (BI) is slated to be a definitive area in the SaaS and analytics industry. The state of BI is observed to be fluid and dynamic, largely due to the disparity in the way various organizations define their IT goals and data modeling processes. According to the latest Forbes report, “organizations that are successful with analytics and BI apps define success in business results, while unsuccessful organizations concentrate on adoption rate first.”

Today, we dive into the most cutting-edge component of BI tech that data analysts are eyeing closely in 2019- Data Journalism or Data Storytelling.

What is Data Storytelling?

Ask data scientists working with visualization tools, “what is the true definition of ‘valid’ data today?”

60% or more of these data scientists would tell that data is valuable and valid if you can garner analytics from them. Data journalism or data storytelling is the art and science of narrating data-driven stories using state of the art visualization to engage the audience, using clean and refined information.

What Tools Make up The Data Journalism BI Tech Stack

Popularity-wise, we can ascertain that heat maps, density pins, radar charts, dot plots, and dumbbells give an edge to storytelling using data.

This is an informative gallery from Microsoft BI tech stack for data storytelling.

In 2019, data visualization would be the center of every BI story designed for businesses.

Recommended: Where Did I Put That Detail? How To Navigate Today’s Data Jungle, Without Getting Hopelessly Lost

One of the top BI tech storytelling platforms, Gap Minder, occasionally puts out data maps and infographics covering the various globally relevant social and economic indices.

We used Gap Minder to find the “Percentage of Internet Users (% of the population) in the G20 countries.

This is what Gap Minder revealed –

It shows the data trends flow from 1990 to 2016 based on data storytelling standards. (Stay Tuned for more in our next article…)

The Era of Immersive BI: Why Embedded Analytics Will Rule in 2019

In an interesting article, BI was pinned down against the raging growth of another data visualization technique—Embedded Analytics. The foreseeable future belongs to BI integrated with embedded analytics for powerful immersive BI. The result would deliver engaging storytelling with Data Journalism.

Insurance, Banking, and Software Sales: Top Customers of Data Journalism Traffic

The insurance and banking industry is leading the pack in the adoption of BI tech for data visualization and embedded analytics. Technology promoters in B2B SaaS have also lined up to leverage their customer data using powerful data journalism. The power of Customer Experience and graphical dashboards deliver a totally different playfield to new-age marketing and sales technology vendors, including in ad sales and mobile marketing.

Check out some really quick Data journalism charts from Microsoft’s BI tech stack.

Recommended: Interview With Raj Minhas, VP, Director Of Interaction And Analytics Laboratory At PARC, A XEROX Company

The post Data Storytelling: An Emerging BI Tech You Can’t Miss in 2019 appeared first on AiThority.