TEL AVIV, Israel, July 8, 2020 /PRNewswire/ --Run:AI, a company virtualizing AI infrastructure, today announced that it is working with the London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare as a technology provider to help them better manage their AI resources and provide elastic resource allocation, visibility and control.

The AI Centre, led by King's College London and based in St Thomas' Hospital, uses an enormous trove of de-identified patient data held by the NHS, including medical images and patient clinical pathway data, to train sophisticated AI learning algorithms. These algorithms are used to create new tools for faster diagnosis, personalized therapies, and more effective screening.

Established by the UK Government's Industrial Strategy Challenge Fund, the AI Centre also includes Imperial College London, Queen Mary University London, four NHS trusts and a number of industry partners. Since the outbreak of the Covid-19 pandemic, the AI Centre has devoted much of its resources to the fight against the novel coronavirus. It recently contributed an AI diagnostic toolthat found anosmia (losing the sense of taste and smell) to be a stronger predictor of COVID-19 infection than fever, and resulted in the UK Government amending its official adviceon suspected infections.

Run:AI ensures that the AI Centre's data scientists can get the full use out of their hardware, guaranteeing that GPU (Graphics Processing Unit) resources are efficiently and elastically allocated to teams that need them. This enables the AI Centre to run more experiments and to speed up time to results, while providing cross-team visibility into how their hardware is being used.

Since installing Run:AI, the AI Centre has slashed the time taken to complete its experiments. The current average is just a day and a half, whereas a simulation of the AI Centre's exact infrastructure running without Run:AI showed an average of over 46 days per experiment - an improvement of 3000%. Over a 40-day period, the researchers ran more than 300 experiments after installing Run:AI compared to just 162 in a simulation of the same environment over the same time period. In addition, actual GPU utilization increased by 2x in the months since Run:AI's platform has been in use.

"Our experiments can take days or minutes, using a trickle of computing power or a whole cluster," said Dr. M. Jorge Cardoso, Associate Professor & Senior Lecturer in AI at King's College London and CTO of the AI Centre. "With Run:AI we've seen great improvements in speed of experimentation and GPU hardware utilization. Reducing time to results ensures we can ask and answer more critical questions about people's health and lives."

"Healthcare is one of the most important and impactful uses of advanced AI, especially now as it can help save lives during the Covid-19 pandemic. We're proud to be working with the London AI Centre to help ensure their important research can get the best use out of their hardware, so they can run more experiments quickly and efficiently," said Omri Geller, CEO and co-founder of Run:AI.

About the London Medical Imaging & AI Centre for Value Based Healthcare

The London Artificial Intelligence Centre for Value Based Healthcare is one of five Centres of Excellence, established as part of the UK Government's Industrial Strategy Challenge Fund, delivered through UK Research and Innovation. Its core purpose is to drive health and economic benefit by making NHS clinical data accessible for artificial intelligence (AI)-driven research and development, and to support the deployment of the resulting products across the NHS. Led by King's College London and based at St Thomas' Hospital, the Centre brings together an ambitious consortium of partners including Imperial College London, Queen Mary University, four NHS Trusts, major industry partners including NVIDIA, Siemens Healthineers, IBM and GSK, and a growing cohort of small-medium enterprises in the UK.

About Run:AI

Run:AI has built the world's first orchestration and virtualization platform for AI infrastructure. By abstracting workloads from underlying hardware, Run:AI creates a shared pool of resources that can be dynamically provisioned, enabling efficient orchestration of AI workloads and optimized utilization of expensive GPUs. Data Scientists can seamlessly consume massive amounts of GPU power to improve and accelerate their research while IT teams retain centralized, cross-site control and real-time visibility over resource provisioning, queuing, and utilization - whether on premises or in the cloud. The Run:AI platform is built on top of Kubernetes, enabling simple integration with existing IT and data science workflows.

Media Contact Lazer Cohen [emailprotected] 347-753-8256

SOURCE Run:AI

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Run:AI Supports AI Centre to Speed Up Machine Learning, Particularly in the Fight Against Covid-19 - PRNewswire

SAN FRANCISCO, July 8, 2020 /PRNewswire/ -- Viz.ai, the leader in Applied Artificial Intelligence for Healthcare, has been named to the prestigious Forbes Top 50 AI list. The list honors the top 50 companies making the most impact using artificial intelligence to drive change and transform industries.

Forbes evaluated hundreds of innovative companies and recognized the top 50 for their use of artificial intelligence to drive outcomes for customers. As a company known for driving innovation in healthcare technology, Viz.ai was selected for its work dedicated to improving treatment times in stroke care and advancing how healthcare is delivered across a hospital network.

"It's not just about the AI," said Eric Eskioglu, MD, FAANS Neurosurgeon, Executive Vice President & Chief Medical Officer Novant Health. "Hospital systems using Viz for stroke care have seen meaningful reductions in treatment times, improvement in outcome scores and reduction in hospital length of stay. Viz is setting the standard for how healthcare can be delivered with operational precision, equity in treatments and outstanding clinical results. It's become how modern healthcare happens."

Viz.ai is now taking its proven patient and operational benefits and applying its technology to other aspects of healthcare, including the response to the pandemic. Viz COVID-19, available to any hospital at no cost, is improving communication, workflow and bed management for hospitals struggling with the COVID-19 crisis. Viz CONSULT is improving imaging, workflow and decision making across multiple new disease states such as Spine, Trauma and Pulmonary Embolism. Viz CLINIC is transforming the doctor visit experience for HCPs and patients and Viz ANALYTICS is enabling dynamic quality improvement through local and national benchmarking.

"We are honored to be recognized by Forbes as one of the leading AI Healthcare companies. It demonstrates the importance of putting patients first and applying the latest technology to improve patient outcomes," said Viz.ai CEO & Co-Founder, Dr. Chris Mansi. "We look forward to making an impact across healthcare ensuring the right patient is seen by the right doctor at the right time, every time."

About Viz.aiViz.ai, is the leader in applied artificial intelligence in healthcare. Viz.ai's mission is to fundamentally improve how healthcare is delivered in the world, through intelligent software that promises to reduce time to treatment and improve access to care. In 2018, the U.S. Food and Drug Administration (FDA) granted a De Novo clearance for Viz LVO, the first-ever computer-aided triage and notification software.Viz.ai is located in San Francisco and Tel Aviv and backed by leading Silicon Valley investors, including Kleiner Perkins, Google Ventures, Innovation Endeavors, CRV, Threshold, DHVC & Greenoaks Capital.

Related Links:www.viz.ai Viz.ai Synchronizing Stroke Care Brochure: https://bit.ly/2Z93sOzViz COVID-19 Product Guide: https://bit.ly/2O7UTgQ

Social Media:LinkedIn: https://www.linkedin.com/company/viz.aiTwitter: https://twitter.com/viz_ai

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Viz.ai Named to Forbes AI 50 List of Most Promising Artificial Intelligence Companies - Yahoo Finance

Yasmina Darveniza, an investor at leading PropTech VC Round Hill Ventures, says AI can have a major impact in real estate

The amount of venture capital money flowing into UK artificial intelligence start-ups hit a record-breaking $3.2 billion in 2019, making it one of the hottest sectors to be in.

This financial boost, along with bolder algorithms, Big Data and better infrastructure, is bringing founders andfunders to the AI equation. Yet according to a recent report, 40 per cent of European firms classified as AI start-ups do not actually use artificial intelligence.

Is AI then just a fad or is it worth the hype?

AI makes it possible for human capabilities to be undertaken by technology at scale. While rules-based programs have existed since the 1950s, AI nowadays usually relates to machine learning providing systems withthe ability to automatically learn from data and improve from experience without being explicitly programmed.

This can be applied to a wide variety of prediction and optimisation challenges, from predicting when patients will get sick to teaching self-driving cars to understand their surroundings.

To utilise this technology, start-up founders need access to talent around applied AI, access to large and proprietary data training sets, and domain knowledge to provide deep insights into the opportunities within an industry. Founders need to identify a sizeable target market and understand the problem theyre trying to solve.

I see no better target market for AI applications than real estate. Not only is it the worlds largest and most important asset class, but also one of the last industries to adopt technological change.

A great example is Israeli start-upSkyline AI,which takes the guesswork out of investmentdecisions by training its technology on the mostcomprehensive data set for US multi-family assets.

Mining data from over 130 sources and analysing10,000-plus data points on each property forthe last 50 years, its tech estimates asset value,predicts future performance and discoversinvestment opportunities.

AI can also optimise both property developmenttime and cost. Nordic start-upSpacemakerAIisa development tool used to maximise the potentialof building sites. Property professionals canuse it to generate and assess billions of possiblesolutions to multi-building developments inhours analysing designs for a range of differentparameters such as sun exposure, noise pollutionand apartment size.

The company has partneredwith leading developers in Europe includingSkanska, OBOS, AF Gruppen and Bouygues tohelp them reduce critical planning time whileincreasing sellable space by up to double digits.

Using Big Data and machine learning algorithms,Iberian start-upCASAFARIenables a higherlevel of efficiency and transparency in assetmanagement. The software provides users withdownloadable historical and descriptive datasets for all property cases and is working tobuild the cleanest, most complete database in itsgeographies. Asset managers can use it to setdatadrivenrental prices and identify the best time tosell assets.

AI has almost unlimited potential across multipleindustries and especially real estate. Not everysolution requires it, but knowing how, when andwhere to effectively use the technology can be akey lever for start-ups and businesses alike.

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Artificial Intelligence: worth the hype? - BusinessCloud

By Balakrishna DR

Globally, the financial services industry has proved to be an enthusiastic adopter of Artificial Intelligence (AI) driven by the availability of data and investment appetite. Creative implementation of AI by start-ups and fintechs has helped further this trend. From personalisation to customer service, fraud detection and prevention to compliance, and risk monitoring to intelligent contract documents, AI has helped banks gain better control and predictability.

Today, customers expect faster, personal, and meaningful services and interactions with their banks and little tolerance for generic unsolicited messages. Therefore, banks must leverage AI to balance the need for privacy and security with personalisation and engagement. That said, the Indian banking sector has some amount of catching up to do.

While Indian banks have explored the use of AI, it has primarily been used to improve customer experience by adding chatbots as an additional interface for customers like SIA by State Bank of India, Eva by HDFC and iPal by ICICI. State-owned banks have been slow to leverage AI, largely because AI implementation requires banks to operate outside of the traditional privacy framework. India still does not have robust data protection and privacy policy. Reserve Bank of India (RBI) needs to take a commanding and dynamic role in framing regulations on emerging technologies, data privacy and ensuring the business interests of the banks.

Banks must adopt new business models simultaneously to integrate AI into their strategic plans and explore the use of AI for analytics and to improve customer experience. However, reliance on legacy systems, lack of data science talent, and cost constraints have impeded seamless adoption of AI. They must focus on three key aspects:

Fraud detection: AI plays a vital role in fraud detection, given the heightened threat of cyberattacks. As per the 2019 RBI annual report, losses due to banking frauds have risen by a whopping 73.8% despite the Governments efforts to curb them. What is more alarming is that banks took an average of 22 months between the occurrence of fraud and its detection, as per RBI data. Considering RBIs zero-liability safety net in the event of cyber frauds, it is imperative banks adopt best-fit practices and technology levers to mitigate these risks. With adoption of real-time payments, there has also been rapid innovation in the digital fraud landscape.

Set against this backdrop, banks must deploy context-sensitive AI solutions to enable advanced and adaptive real-time monitoring of their payment networks. These AI solutions additionally leverage relevant data points to assess transaction risk, true identity-matching, and identification of complex typologies and patterns.

Digitisation of processes: The tremendous proliferation of mobile devices and the internet can be leveraged to enable the superior user experience and analytics-based functionalities that give consumers an insight into their spending patterns and provide recommendations on investment and risk profiles. For instance, digitising the KYC process to eliminate the need for physical document submission and verification is something that traditional banks still do not offer. This can be simplified by utilising AI-based computer vision technology to verify documents, Optical/Intelligent Character Recognition (OCR/ICR) technologies to digitise scanned documents, and Natural Language Processing (NLP) to make sense of them.

Decision making: AI is a great fit in areas where decisions are based on available structured and unstructured data. For example, it can help predict potential loan defaulters and offer loss mitigation strategies that will work for them. It can help determine the best time to approach a customer to sell a new product. AI-based smart environments can collate data from multiple sources and drive an inference and enable SMEs to take decisions. AI can also improve straight-through processing using Intelligent Automation to automate repetitive processes that need decision making.

Given the magnitude of the challenge, it might make sense for banks to come together to establish a consortium for knowledge sharing on AI. This would also help Indias numerous regional and cooperative banks that are behind on the technology curve. A consortium could help uplift these small banks and enable them to be integrated seamlessly into a broader nationwide secure banking network. Whichever way it happens, AI in Indian banking is only set to grow.

The author is Senior VP, Service Offering Head Energy, Communications, Services and AI & Automation Services

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Banking on AI: The time is ripe for Indian banks to embrace artificial intelligence - The Financial Express

LONDON--(BUSINESS WIRE)--Technavio has been monitoring the artificial intelligence (AI) market in BFSI sector and it is poised to grow by USD 11.94 bn during 2019-2023, progressing at a CAGR of over 32% during the forecast period. The report offers an up-to-date analysis regarding the current market scenario, latest trends and drivers, and the overall market environment.

Although the COVID-19 pandemic continues to transform the growth of various industries, the immediate impact of the outbreak is varied. While a few industries will register a drop in demand, numerous others will continue to remain unscathed and show promising growth opportunities. Technavios in-depth research has all your needs covered as our research reports include all foreseeable market scenarios, including pre- & post-COVID-19 analysis. Download The Latest Free Sample Report of 2020-2024

The market is concentrated, and the degree of concentration will accelerate during the forecast period. Amazon Web Services Inc., Google LLC, IBM Corp., Microsoft Corp., and Oracle Corp. are some of the major market participants. To make the most of the opportunities, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

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Focus on autonomous banking has been instrumental in driving the growth of the market.

Technavio's custom research reports offer detailed insights on the impact of COVID-19 at an industry level, a regional level, and subsequent supply chain operations. This customized report will also help clients keep up with new product launches in direct & indirect COVID-19 related markets, upcoming vaccines and pipeline analysis, and significant developments in vendor operations and government regulations. https://www.technavio.com/report/report/global-artificial-intelligence-ai-market-in-BFSI-sector-industry-analysis

Artificial Intelligence (AI) Market in BFSI Sector 2019-2023: Segmentation

Artificial Intelligence (AI) Market in BFSI Sector is segmented as below:

To learn more about the global trends impacting the future of market research, download a free sample: https://www.technavio.com/talk-to-us?report=IRTNTR31823

Artificial Intelligence (AI) Market in BFSI Sector 2019-2023: Scope

Technavio presents a detailed picture of the market by the way of study, synthesis, and summation of data from multiple sources. The artificial intelligence (AI) market in BFSI sector report covers the following areas:

This study identifies the growing focus on personalized experience as one of the prime reasons driving the artificial intelligence (AI) market growth in BFSI sector during the next few years.

Technavio suggests three forecast scenarios (optimistic, probable, and pessimistic) considering the impact of COVID-19. Technavios in-depth research has direct and indirect COVID-19 impacted market research reports.Register for a free trial today and gain instant access to 17,000+ market research reports.

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Artificial Intelligence (AI) Market in BFSI Sector 2019-2023: Key Highlights

Table of Contents:

PART 01: EXECUTIVE SUMMARY

PART 02: SCOPE OF THE REPORT

PART 03: MARKET LANDSCAPE

PART 04: MARKET SIZING

PART 05: FIVE FORCES ANALYSIS

PART 06: MARKET SEGMENTATION BY END-USER

PART 07: CUSTOMER LANDSCAPE

PART 08: GEOGRAPHIC LANDSCAPE

PART 09: DECISION FRAMEWORK

PART 10: DRIVERS AND CHALLENGES

PART 11: MARKET TRENDS

PART 12: VENDOR LANDSCAPE

PART 13: VENDOR ANALYSIS

PART 14: APPENDIX

PART 15: EXPLORE TECHNAVIO

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Technavio is a leading global technology research and advisory company. Their research and analysis focus on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions. With over 500 specialized analysts, Technavios report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavios comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

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COVID-19 Impact and Recovery Analysis | Artificial Intelligence (AI) Market In BFSI Sector 2019-2023 | Focus On Autonomous Banking to Boost Growth |...

Representational image

Experts at University of British Columbia in Canada who are building an Artificial Intelligence-powered COVID-19 diagnosis tool with the help of resources from Amazon Web Services (AWS) are seeking lung scan images from India to refine their open source model, a researcher involved with the project said.

This tool is important because it becomes easier for doctors the world over to treat a patient if they know what disease they are suffering from and how badly that disease has infected that person.

The same goes with COVID-19 patients. Knowing that a person is COVID-19 positive can help, but this is not all that doctors want to know. They would do better if they knew how deep the infections were and how the patients were likely to respond to the treatments.

Now researchers know that lung images of COVID-19 patients can give them some clue to finding answers to these important questions.

That is the reason why a project was set up at the Cloud Innovation Centre (CIC) at the University of British Columbia (UBC) with the goal to develop and deploy an open source AI model capable of analysing CT scans of COVID-19 infected patients.

It aims to empower radiologists by providing metrics and statistical information about the infection that cannot normally be assessed by the human eye alone. The CIC at UBC is a public-private collaboration between UBC and AWS.

Recent literature suggests that the percentage of well-aerated-lung correlates to clinical outcomes, such as the need for ventilator support, ICU admission and death.

But the percentage of lung involvement, and inversely the percentage of well aerated lung, is difficult to accurately measure without advanced software tools, such as AI, said Savvas Nicolaou, Director of Emergency and Trauma Imaging at Vancouver General Hospital, as well as a Professor of Radiology at the University of British Columbia.

"We hope that utilising a machine to accurately calculate the lung involvement ratio and absolute volume will be a valuable metric for researchers to use to prognosticate patients with COVID-19 and other respiratory illnesses," Nicolaou told IANS in a video call.

The team leading the project worked with health centres around the world to assemble one of the largest international COVID-19 CT-scan datasets, but it could not immediately gather data from India at the start of the project due to the strict restrictions put in place in the country to fight the pandemic.

The dataset contains CT studies from countries such as Iran, Italy, Saudi Arabia, South Korea, and Canada to increase model generalisability, minimise bias, and establish an accurate model for any site.

The project dataset consists of COVID-19 positive scans as well as scans of patients with similar symptoms but are not COVID-19 positive.

By May this year, the "COVID-L3-Net" model was built on more than 1,100 CT scans. The team has collected another 3,100 scans from around the world that will be labeled to make the model even more accurate.

"The team welcomes scans from India. If radiologists or any medical professionals want to share their data sets and scans with the team, they need to reach out through the UBC Cloud Innovation Centre website," Nicolaou said.

"The team is also looking for researchers to test the model since it is in beta, and to receive feedback on the model," he said.

The data collaboration was possible due to an open-source tool called SapienSecure which was developed and released by a company named SapienML last year.

This open source app standardises the data de-identification of Personal Identifiable Information in medical imaging and helps to integrate directly into Amazon's storage service, Amazon S3, in the AWS Canada (Central) Region.

A team of more than 30 Vancouver General Hospital radiologists and UBC medical students coded the images, using software from MD.ai Inc.

Teams can remotely login, label and work on scans from home during this COVID-19 pandemic, all powered by Amazon compute instances.

"Right now, the team has released the model in pre-beta, and will be moving to beta soon. The goal is to have the model released in early September," Nicolaou said.

"The models keep getting better with more data, so we continue to collect more CT Scans from around the world," he added.

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Artificial Intelligence Powered COVID-19 Diagnosis Tool to Seek Lung Scan Images from India - The Weather Channel

A Long Island artificial intelligence startup has built software aimed at pinpointing U.S. counties where the COVID-19 outbreak is likely to be most deadly.

In a June report, the data-mining company, Akai Kaeru LLC, forecast spiking COVID-19 mortality with the heaviest concentrations in counties of the Southeast, including Mississippi, Georgia and Louisiana, said co-founder and chief executive Klaus Mueller.

Nationwide, the software found 985 out of all 3,007 U.S. counties are at risk.

"These patterns identify groups of counties that have a steeper increase in the death-rate trajectory," he said.

Closer to home, the software found Nassau and Suffolk counties are likely to be relatively stable, but Westchester and Rockland counties are potential tinderboxes that could tip into crisis, said Mueller, a computer science professorat Stony Brook University.

The factors making Westchester and Rockland more vulnerable to a spike in mortality include areas with more crowding and fewer residents with access to cars, he said.

"They need to be very careful with reopening," Mueller said of the northern suburbs. "It just takes a spark for there to be a second wave."

At the same time, he said, Long Island "is not out of the woods" and abandoning policies like social distancing could lead to a new surge.

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The software analyzes more than 500 attributes related to demographics, economics, infrastructure, race and ethnicity as well as deaths and other health data directly related to COVID-19.

Dr. Isaac Weisfuse, an epidemiologist and adjunct professor at Cornell University's public health program, said that data-mining software is used by public health departments.

If the software provides sufficient warning, he said, preventive measures like screening and mask-wearing policies can be instituted.

"It's more valuable if it's accurate two months before, but it's still valuable two weeks before," he said.

The Centers for Disease Control and Prevention aggregates mortality forecasts from about two dozen software programs and expects 140,000 to 160,000 total reported COVID-19 deaths in the United States by July 25.

While many COVID-19 models provide specific fatality forecasts at the state level, the Akai Kaeru software is one of the few that assesses risk at the county level.

Mueller said that based on the one-month snapshots, the software is finding that counties at the highest risk have a death rate that grew two- to three times more than the United States,overall.

In June, the fatality rate for U.S. COVID-19 related deaths was 24.1 per 100,000 population, he said.

Aside from finding geographies in jeopardy, the software is able to unearth specific and sometimes surprising combinations of factors that appear to be connected to counties with higher death rates.

For instance, counties with low poverty levels, high homeownership rates, but high levels of housing debt were found to be at high risk.

"The more housing debt you have, the more death you have," Mueller said.

Other counties at risk had a combination of residents who were sleep-deprived (according to data from the CDC) and had low levels of education and low rates of health insurance coverage.

Another group of counties had few Asian residents but high overall minority populations, including impoverished Black children.

Rural counties with high poverty rates and an aging population also were deemed at risk.

"One of the defining characteristics is we focus on explainability," said Eric Papenhausen, chief technology officer and co-founder of the company. "You can create a narrative around it," which can lead to changes in public policy.

Akai Kaeru is based at the Center of Excellence in Wireless and Information Technology on the Stony Brook University campus.

The 4-year-old company, whose name is Japanese for red frog, has raised $1 million in funding from the National Science Foundation's Small Business Innovation Research program and about $200,000 through the New York State Strategic Partnership for Industrial Resurgence program and the New York State Center for Advanced Technology.

The COVID-19 software is a demonstration project for the company, whose data-mining software can be applied to a variety of tasks, including assessing mortgage risk, speeding drug discovery and investment analysis.

Another startup, Manhattan-based Dataminr, is seeking to use social media posts as a leading indicator of COVID-19 infections at the county level.

Artificial intelligence refers to the ability of software programs to learn and perform actions previously reserved for humans.

Mueller said his company's "explainable AI" is not a black box and can provide insight into how the software reached its conclusions.

Ken Schachter covers corporate news, including technology and aerospace, and other business topics for Newsday. He has also worked at The Miami Herald and The Jerusalem Post.

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LI startup predicts where COVID-19 will spike - Newsday

The human brain has advanced over time in countering survival instincts, harnessing intellectual curiosity, and managing authoritative ordinances of nature. When humans got an idea about the dynamics of the environment, we started with our quest to replicate nature.

While the human brain discovers ways to go beyond our physical capabilities, the combination of mathematics, algorithms, computational methods, and statistical models accumulated momentum after Alan Mathison Turing built a mathematical model for biological morphogenesis, and published a seminal paper on computing intelligence.

Today, AI has developed from data models for problem-solving to artificial neural networks, a computational model predicated on the structure and functions of human biological neural networks.

The brain, customarily perceived as an organ of the human body, should be understood as a biologically predicated form of artificial intelligence (AI). This proposition was surmised by the progenitors of AI in the 1950s, though it has been generally side-lined over the course of AIs history. However, developments in both neuroscience and more conventional AI make it fascinating to consider the issue anew.

The history of neuroscience has shown both tendencies from its inception, not least in terms of the alternative functions performed by the characteristic technologies of the AI field.

Understanding the complete impacts of this distinction needs eluding from the reductionist problematic that perpetuates to haunt philosophical discussions of neurosciences aspirations as a mode of inquiry

The early prospect, which will help to build machines possessing intelligence of humans, found inspiritment in three main directions.

Firstly, proof that the functioning of the human brain and nervous system, while astonishingly perplexed from a biological perspective, is predicated on elementary all-or-nothing procedures of the type that can facilely be copied by digital electronic circuits.

Secondly, the growth of symbolic logic and formal languages that are able to communicate immense components of higher mathematics, recommending that all human reasoning might be ultimately abbreviated to similar manipulating strings of symbols according to sets of rules. Such formal operations can probably easily be imitated by a digital computer.

Thirdly, the outlook of creating faster electronic calculating devices. With regard to this, developments since the 1950s have rarely been saddening. The density of switching elements of todays microchips surpasses that of neurons in the brain.

Artificial intelligence makes industrial machines and equipment precise, credible and self-healing, making strides calibrated performance imitating human action. AI incorporates robotic controls, vision-based sensing, and geospatial systems in order to automate advanced frameworks. It improves disease detection and prevention along with its treatment, amplifies engineering systems and handles self-organizing supply chains.

We, humans, are dependent on machines for decision-making for various processes like underwriting, recruitment, fraud detection, maintenance, etc. Real Core Energy deploys machine learning that assesses production as well as performance factors to better conduct oil drilling operations and investment decisions.

Though artificial intelligence has become indispensable in almost all fields today, the presiding approaches to artificial intelligence are based in false conceptions about the nature of the mind and of the brain as a biological organ.

Sadly, the superficial models of the brain and mind, which were the initial Kickstarter of artificial intelligence, have now become the paradigm for everything called cognitive science, as well as a huge part of neurobiology. It has become a standard protocol to levy methods, concepts, models and vocabulary from the domain of artificial intelligence, computer science onto the research of the brain and the mind. It is difficult to discover a scientific paper on these subjects which does not contain terms like computing, processing, circuits, storage and retrieval of information, encoding decoding etc.

Computational neuroscience connects human intelligence and artificial intelligence by developing theoretical models of the human brain for multiple studies on its functions, including vision, motion, sensory control, and learning.

Studies in human cognition are uncovering a deeper comprehension of our nervous system and its compound processing abilities. Models that provide high-level insights into memory, data processing, and speech/object recognition are simultaneously reshaping AI.

The integration of human intelligence with artificial intelligence will evolve computers into superhumans or humanoids that go far beyond human abilities. However, it needs computing models that combine visual and natural language processing, just how the brain functions, for comprehensive communication.

Neuroscience has made significant contributions to strengthen AI research and gain its increasingly important relevance. In planning for the future amalgamation of the two fields, it is essential to value that the past contributions of neuroscience to AI have hardly consisted of a simple shift of complete solutions which can be simply re-implemented in machines. Rather, neuroscience has often been useful in a precise way, facilitating algorithmic-level questions about qualities of animal learning and intelligence of interest to AI researchers and offering initial drives toward applicable mechanisms.

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The Amalgamation of Human Brain and Artificial Intelligence - Analytics Insight

LONDON--(BUSINESS WIRE)--ContractPodAi, the award-winning provider of AI-powered contract lifecycle management solutions, today announced that it is partnering with Bowmans, one of Africas leading corporate law firms, to introduce Bowmans clients to its advanced technology solution. All this allows corporate legal teams to work smarter, faster and with far greater impact during the contracting process.

ContractPodAi is one of the worlds most robust contract lifecycle management (CLM) technologies, providing corporate legal counsels with a platform that provides end-to-end contract management capabilities like a smart contract repository, contract automation, document e-signatures, seamless workflows, third party contract review, negotiating and collaboration tools, and AI-based analytics.

As part of its digitisation strategy, Bowmans is strengthening its technology solutions toolkit. The firm is partnering with ContractPodAi because it offers a robust and graphically intuitive contract management system that streamlines document automation processes.

Craig Kennedy, Head of Technology, Media and Telecommunications at Bowmans, said: Part of the value we add to our clients businesses is the ability to support them in exploring and identifying suitable digital solutions to streamline their legal services.

With ContractPodAi, we saw an opportunity to help them to focus on strategic initiatives by implementing a technology solution that replaces time-consuming manual efforts.

ContractPodAi offers customers intelligent AI functionality, built on the trusted IBM Watson, and Microsoft Azure AI platforms, right out-of-the box. Its like getting the safety of IBM and Microsoft with the speed of a startup. A big part of whether CLM technology is successful within a company is its adoption with the business users, and legal team. Beyond the intuitive graphical user interface, a client success manager (CSM) supports every customer. Digital transformation is a challenge for any industry, and legal is no exception. As such the CSM facilitates adoption and encourages internal advocacy and education of every rollout.

We are thrilled to partner with an innovative African law firm like Bowmans to introduce our contract management solution to the African market, said Sarvarth Misra, co-founder and CEO, ContractPodAi. It is exciting to work with a firm that embraces the use of technology and is dedicated to making their clients successes a priority.

Learn how ContractPodAi is empowering legal teams across the world at ContractPodAi.com.

About ContractPod Technologies (ContractPodAi)

A pioneer in the legal transformation space, ContractPodAi is now one of the worlds fastest growing legal tech companies. Customers include some of the worlds largest and highly regarded corporations. ContractPodAi is an award-winning easy to use, intuitive and affordable end-to-end contract lifecycle management solution aimed at corporate legal departments. It enables users to assemble, automate, approve, digitally sign and manage all their contracts and documents from one place.

Our platform is built in partnership with some of the most trusted technologies in the industry including IBM Watson AI, Microsoft Azure, DocuSign and Salesforce. ContractPodAi is headquartered in London and has global offices in San Francisco, New York, Glasgow, Mumbai and Toronto. More information is available at ContractPodAi.com.

About Bowmans

With over 400 specialist lawyers, Bowmans draws on its unique knowledge of the business and socio-political environment in Africa to advise on a wide range of legal issues.

Everywhere it operates, Bowmans offers its clients a service that uniquely blends expertise in the law, knowledge of the local market and an understanding of their businesses. The firms aim is to assist its clients to achieve their objectives as smoothly and efficiently as possible while minimising the legal and regulatory risks.

Clients include corporates, multinationals and state-owned enterprises across a range of industry sectors as well as financial institutions and governments.

Read more:
ContractPodAi and Bowmans Partner to Bring Artificial Intelligence-Powered Contract Management to the African Market - Business Wire

"It's easy for AI to come up with something novel just randomly. But it's very hard to come up with something that is novel and unexpected and useful." - John Smith, manager of Multimedia and Vision at IBM Research

Platforms such as Googles Smart Display and Dynamic Search and Facebooks Dynamic Creatives enable brands to automate the process of tailoring ads for different audiences and to do so at a highly granular level. In theory, this enables us to improve engagement and conversion by getting the right message to the right person at the right time.

But AI cannot be truly creative in the sense of using imagination to develop truly original ideas and make something. AI systems are limited by the original datasets humans give them to learn from. So, the question shouldnt be technology or creativity, but rather how AI can help creatives to meet their goals.

Getting it right isnt as simple as testing various combinations of assets to achieve the highest relevance for each person and to ensure the best results for the campaign goal. Its not just about putting together the right images and copy to address the needs of the user, but also about ensuring that the ad the person sees is interesting and emotionally engaging. Thats where human creativity comes in.

So where do brands, agencies and marketing teams go from here?

1. Start thinking about AI as an assistant for the creative team rather than a potential replacement for human insight and emotional intelligence. AI is invaluable and cost-effective for the rapid gathering of data and testing of different creative combinations, freeing up time for human creatives to dream up original thoughts and build emotionally engaging creative assets. Creatives and marketers will need to rapidly upskill to keep ahead of the tech advancements.

2. Marketers still need their creative agencies, perhaps more than ever. They should find ways to bridge the gaps between creative agencies and data & analytics teams and agencies. This will help them use data to drive better creative, while leveraging human strengths around cultural nuances, understanding human motivation, and original thinking.

3. Creative teams will need to move beyond the one killer concept or the one big idea towards developing multiple concepts that can be tested across various audience segments. The good news is that we can now try numerous ideas cheaply and rapidly without focus groups or surveys. This enables creatives to quickly create engaging assets and messages answering to different stages of the customer journey, and different consumer behaviours, demographics, interests and so forth.

4. Its time for media owners and digital media agencies to work more closely with creative agencies. A good media strategist will be able to offer a lot more value upfront when creatives are brainstorming rather than at the end. The role is no longer simply to select the best channels and propose the most suitable ad units, but also to help creatives to understand the potential of various channels and machine learning.

To close, here are some practical tips for AI-enabled creativity:

More:
Artificial intelligence should be used to augment human creativity - not replace it - Bizcommunity.com