Category Archives: Artificial Intelligence

NEW YORK June 1, 2022 (Newswire.com)

Gartner predicts that artificial intelligence (AI) software will reach $62 billion this year alone. As attention on this industry continues to grow, companies in the space have a unique opportunity to capitalize and build brand awareness.

As an industry leader inpress release distribution services, Newswire is helping its client in the AI industry do just that.

Through a blend of its comprehensive program, robust SaaS platform and enriched distribution network, Newswire continues to help this client distribute the right message to the right audience at the right time and earn valuable media mentions.

As the AI industry expands, brands in the space need to find ways to stand out in a crowded marketplace and an excellent way to do so is through press release distribution, said Charlie Terenzio, CMO and SVP of Media and Marketing Communications at Newswire. Press releases are an authoritative piece of content and when written and distributed correctly can help brands secure media opportunities thatll capture the attention of their target audience.

For its client in the AI industry, Newswire has facilitated media mentions in relevant and popular publications such asAiThority,Authority Magazine,Digital Journal andMartech Series.

These earned media mentions put Newswires client in the spotlight which in turn builds brand awareness, attracts website visitors, improves SEO performance, increases sales and more.

Additionally, the potential benefits of press release distribution also include but arent limited to:

Turning owned media into earned media through strategic press release distribution can unlock valuable opportunities that brands of all sizes and industries can use to build strong foundations for current and future success, added Terenzio.

To learn more about how Newswires integrated solutions are helping brands craft newsworthy content, deliver strategic media pitches, earn media mentions, grow their audience, expand their reach, and implement an effective go-to-market strategy, visitNewswire.com today.

About NewswireNewswire is a technology company that providespress release distribution, media database and media monitoring technology that powers the media advantage: greater brand awareness, online visibility, SEO recognition, site traffic and increased sales by providing self-serve or full-service technology that automates press release distribution, media outreach, and monitoring that drives SEO recognition leading to more sales. Through its disruptive technology platform, relentless commitment to customer satisfaction, and passion for customer performance Newswire is automating media and marketing communications for large and small businesses all over the world.

To learn more about press release distribution or theMedia Advantage Plan, visitNewswire.com or check out why our customers have named us#1 for Customer Satisfaction in our industry for four years in a row.

Contact Information:

Charlie TerenzioCMO and SVP of Media and Marketing CommunicationsNewswireOffice: 813-480-3766Email: [emailprotected]Source: Newswire

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Original Source:Company in the Artificial Intelligence Industry Earns Media Mentions With Newswire's Help

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Company in the Artificial Intelligence Industry Earns Media Mentions With Newswire's Help - Digital Journal

The Global Artificial Intelligence Edge Device Market size is estimated to be USD 1.73 billion in 2019 and is predicted to reach USD 13.21 billion by 2030 with a CAGR of 20.3% from 2020-2030.

Artificial Intelligence Edge Device consists of artificial intelligence software which is operated on cloud based software and can process large amount of data in short span of time. It has sensors and other required elements used for data processing and it does not need to be connected to other devices in order to work efficiently as it can work independently.

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Top Companies: Intel Corporation, Huawei Technologies Co., Ltd., MediaTek Inc., Xilinx Inc., NVIDIA Corporation, Microsoft Corporation, Samsung Electronics Co., Ltd., Imagination Technologies Limited, and Google Inc and others.

Market Dynamics and Trends

There have been constant developments in the existing technology coupled with the constant development of infrastructure to ease the operations on a daily basis. Furthermore, increased awareness about artificial intelligence, increase in smart and wearable devices, coupled with increased use of IoT is expected to accelerate the growth of artificial intelligence edge device.

The other factors include technological advancement, increase in work load, easy data process and computation coupled with increase in integration with other systems are also expected to promote the market growth. However, increase in cyber attacks and data security concern are expected to hamper the artificial intelligence edge device market growth over the forecast period. Moreover, wide application and increased connectivity among devices are further expected to create ample opportunities in the artificial intelligence edge device in near future.

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Market Segmentations and Scope of the Study:

The global artificial intelligence edge device market share is analyzed on the basis of device, processors, process, end user and geography. On the basis of devices, the market is segmented into Smartphones, Cameras, Robots, Wearables, Smart speakers, Automotive, and Smart mirror. On the basis of processors, the market is divided into CPU, GPU, ASIC, and Others. On the basis of process, the market is divided into training and inference. On the basis of end user, the market is divided into Consumer electronics, Smart home, Automotive and transportation, Government, Healthcare, Industrial, Aerospace & Defence, Construction, and Others. Geographic breakdown and analysis of each of the aforesaid segments includes regions comprising North America, Europe, Asia-Pacific, and RoW.

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We have been servicing over 1000 customers globally that includes 90% of the Fortune 500 companies over a decade. Our analysts are constantly tracking various high growth markets and identifying hidden opportunities in each sector or the industry. We provide one of the industrys best quality syndicate as well as custom research reports across 10 different industry verticals. We are committed to deliver high quality research solutions in accordance to your business needs. Our industry standard delivery solution that ranges from the pre consultation to after-sales services, provide an excellent client experience and ensure right strategic decision making for businesses.

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Artificial Intelligence Edge Device Market 2022 is Booming across the Globe by Share, Size, Growth, Segments and Forecast to 2030 - Digital Journal

London, United Kingdom, Wed, 01 Jun 2022 06:19:40 / PhantMedia. / Global Artificial Intelligence In Transportation Marketto surpass USD 29.389 billion by 2031 from USD 1950.14 billion in 2021 at a CAGR of 25.36% within the coming years, i.e., 2021-3

Fatpos Global added a new report into their database named Artificial Intelligence In Transportation Market Segments: By Machine Learning Deep Learning Computer Vision Context Awareness NLP By Application Semi & Full-Autonomous HMI, Platooning By Offering Hardware Software By Process Signal Recognition Object Recognition Data Mining 20212031 Global Industry Perspective, Comprehensive Analysis, and Forecast. The study offers historical data from 2016 to 2021, as well as a forecast for 2022 to 2031 based on revenue (USD Million).

Global Artificial Intelligence In Transportation Marketto surpass USD 29.389 billion by 2031 from USD 1950.14 billion in 2021 at a CAGR of 25.36% within the coming years, i.e., 2021-3

Artificial Intelligence In Transportation Market Summary:

Complete reportSample PDFCopy is ready: (Including List of Tables, Charts, Figures, TOC)published by Fatpos Global.

Global Artificial Intelligence In Transportation Market: Drivers and Restrains

Global Artificial Intelligence In Transportation Market: Segment Breakdown

The research report divides the market into segments based on region (country), manufacturer, product type, and application. During the forecast period of 2021 to 2031, each product type gives information on the production. Consumption is also provided for the Application sector for the predicted period of 2021 to 2031. Understanding the segments aids in determining the importance of various market growth variables.

The Key Players Mentioned in the Artificial Intelligence In Transportation Market Research Report include:

Continental AG

Competitive Landscape:

Due to the vast number of players in this industry, the Global Artificial Intelligence In Transportation Market is highly consolidated. The research goes into great detail on these companies' present market position, previous performance, production and consumption trends, demand and supply graphs, sales networks, growth potential, and distribution methods. The study examines prominent market participants' strategic approaches to growing their product offerings and strengthening their market position.

Request a Discounton the Artificial Intelligence In Transportation Report (with COVID-19 Impact Study)

Exploring a Few Radical Features of the Artificial Intelligence In Transportation Market Report:

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Some of the key questions answered in this report:

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Artificial Intelligence In Transportation Segmentation 2022: Size, Shares, Top Region Records, Industry Outlook, Driving Factors By Manufacturers,...

The demand for agricultural products is surging in countries such as Brazil, India, the U.S., and China due to the rapid urbanization, surging disposable income, and changing consumption patterns of the booming population. On account of the soaring demand, these countries are leveraging artificial intelligence (AI) to increase their overall agricultural productivity. Owing to this reason, the AI in agriculture market is expected to progress at a robust CAGR of 24.8% during 20202030. According to P&S Intelligence, at this rate, the value of the market will rise from $852.2 million in 2019 to $8,379.5 million by 2030.

In recent years, the usage of smart sensors has increased tremendously in agriculture, as they enable farmers to map their fields accurately and apply crop treatment products to the areas that need them. Moreover, the development of several operation-specific sensors, including airflow sensors, location sensors, weather sensors, and soil moisture sensors, is assisting farmers in monitoring and optimizing their yields. Additionally, technology companies are developing smart sensors that are adaptable to the altering environmental conditions.

Additionally, the agrarian community is deploying drones in large numbers to monitor the growth and health of crops. Farmers use drones to scan the soil health, estimate the yield data, draft irrigation schedules, and apply fertilizers. Besides, the increasing support from the government has led to the widescale adoption of drones for modernizing agricultural practices. For example, in January 2019, the government of Maharashtra, India, partnered with the World Economic Forum (WEF) to enhance the agricultural yield by gathering insights about the farms through drones.

How Are AI-Powered Smart Sensors Improving Agricultural Practices?

Further, AI is being used in the agriculture sector to monitor the livestock in real-time. The utilization of AI solutions, such as facial recognition and image classification integrated with feeding patterns and condition score, enables dairy farms to individually monitor all the behavioral aspects of a herd. Moreover, farmers are using machine vision to recognize facial features and hide patterns, record the behavior and body temperature, and monitor the food and water intake of the livestock.

North America witnesses large-scale deployment of the AI technology in agricultural activities owing to the early adoption of computer vision and machine learning (ML) for soil management, precision farming, greenhouse management, and livestock management. Moreover, the increasing adoption of the internet of things (IoT) technology bolstered with computer vision will promote the application of AI solutions by the farming community. Besides, the existence of numerous technology vendors and sensor manufacturers in the region promotes the usage of the AI technologies in the agricultural space.

Furthermore, the Asia-Pacific (APAC) region is expected to adopt AI-enabled agricultural solutions at the fastest pace in the coming years. The high adoption rate of AI in China, Australia, India, and Japan will contribute significantly to the APAC AI in agriculture market in the future. Moreover, the entry of the Alibaba Group in the agricultural solution business, with its AI technology, will increase the penetration of these solutions in the Chinese agricultural industry. Additionally, India is utilizing such solutions due to the escalating effort by multinational companies (MNCs) and the government to spread awareness regarding data sciences and farm analytics among farmers.

Thus, the growing need to increase the crop yield and improve livestock management will fuel the adoption of AI-enabled solutions in the agricultural space.

Source: P&S Intelligence

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Farmers Increasing Their Crop Yield with Artificial Intelligence - Farmers Review Africa

Artificial intelligence is todays most discussed and debated technology, generating widespread adulation and anxiety, and significant government and business interest and investments. But six years after DeepMind's AlphaGo defeated a Go champion, countless research papers showing AIs superior performance over humans in a variety of tasks, and numerous surveys reporting rapid adoption, what is the actual business impact of AI?

Human intelligence communicating with the artificial kind. (Photo by Jonas Gratzer/LightRocket via ... [+] Getty Images)

2021 was the year that AI went from an emerging technology to a mature technology... that has real-world impact, both positive and negative, declared the 2022 AI Index Report. The 5th installment of the index measures the growing impact of AI in a number of ways, including private investment in AI, the number of AI patents filed, and the number of bills related to AI that were passed into law in legislatures of 25 countries around the world.

There is nothing in the report, however, about real-world impact as I would define itmeasurably successful, long-lasting and significant deployments of AI. There is also no definition of AI in the report.

Going back to the first installment of the AI Index report, published in 2017, still does not yield a definition of what the report is all about. But the goal of the report is stated upfront: the field of AI is still evolving rapidly and even experts have a hard time understanding and tracking progress across the field. Without the relevant data for reasoning about the state of AI technology, we are essentially flying blind in our conversations and decision-making related to AI.

Flying blind is a good description, in my opinion, of gathering data about something you dont define.

The 2017 report was created and launched as a project of the One Hundred Year Study on AI at Stanford University (AI100), released in 2016. That studys first section did ask the question what is artificial intelligence? only to provide the traditional circular definition that AI is what makes machines intelligent, and that intelligence is the quality that enables an entity to function appropriately and with foresight in its environment.

So the very first computers (popularly called Giant Brains) were intelligent because they could calculate, even faster than humans? The One Hundred Year Study answers Although our broad interpretation places the calculator within the intelligence spectrumthe frontier of AI has moved far ahead and functions of the calculator are only one among the millions that today's smartphones can perform. In other words, anything a computer did in the past or does today is AI.

The study also offers an operational definition: AI can also be defined by what AI researchers do. Which is probably the reason this years AI Index measures the real-world impact and progress of AI, among other indicators, by the number of citations and AI papers (defined as AI by the papers authors and indexed with the keyword AI by the publications).

Moving beyond circular definitions, however, the study provides us with a clear and concise description of what prompted the sudden frenzy and fear around a term that was coined back in 1955: Several factors have fueled the AI revolution. Foremost among them is the maturing of machine learning, supported in part by cloud computing resources and wide-spread, web-based data gathering. Machine learning has been propelled dramatically forward by deep learning, a form of adaptive artificial neural networks trained using a method called backpropagation.

Indeed, machine learning (a term coined in 1959) or teaching a computer to classify data (spam or not spam) and/or make a prediction (if you liked book X, you would love book y), is what todays AI is all about. Specifically, since its image classification breakthrough in 2012, its most recent variety or deep learning, involving data classification of very large amounts of data with numerous characteristics.

AI is learning from data. The AI of the 1955 variety, which generated a number of boom-and-bust cycles, was based on the assumption that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. That was the vision and, by and large, so far it hasnt materialized in a meaningful and sustained way, demonstrating significant real-world impact.

One serious problem with that vision was that it predicted the arrival in the not-so-distance future of a machine with human intelligence capabilities (or even surpassing humans), a prediction reiterated periodically by very intelligent humans, from Turing to Minsky to Hawking. This desire to play God, associated with the old-fashioned AI, has confounded and confused the discussion (and business and government actions) of present-day AI. This is what happens when you dont define what you are talking about (or define AI as what AI researchers do).

The combination of new methods of data analysis (backpropagation), the use of specialized hardware (GPUs) best suited for the type of calculations performed, and, most important, the availability of lots of data (already tagged and classified data used for teaching the computer the correct classification), is what led to todays AI revolution.

Call it the triumph of statistical analysis. This revolution is actually a 60-year evolution of the use of increasingly sophisticated statistical analysis to assist in a wide variety of business (or medical or governmental, etc.) decisions, actions, and transactions. It has been called data mining and predictive analytics and most recently, data science.

Last year, a survey of 30,000 American manufacturing establishments found that productivity is significantly higher among plants that use predictive analytics. (Incidentally, Erik Brynjolfsson, the lead author on that study has also been a steering committee member of the AI Index Report since its inception). It seems that its possible to find a measurable real-world Impact of AI, as long as you define it correctly.

AI is learning from data. And successful, measurable, business use of learning from data is what I would call Practical AI.

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What Is AI? Understanding The Real-World Impact Of Artificial Intelligence - Forbes

Welcome to the age of blended workforces, where intelligent machines and humans combine to accelerate business success.

Human Vs. Artificial Intelligence: Why Finding The Right Balance Is Key To Success

In short, now that we have increasingly capable robots and artificial intelligence (AI) systems capable of taking on tasks that were previously the sole domain of humans its easier than ever for organizations to leverage intelligent machines. But this leaves employers with some major questions to answer: how do we find the right balance between intelligent machines and human intelligence? What roles should be given over to machines? And which roles are best suited to humans?

The first step: Understanding what machines can do

Particularly in traditional companies, business leaders often arent up to speed on the sheer range of tasks that todays AIs and intelligent robots can take on. (In fact, I spend a lot of time educating executives in this area.) This knowledge is key to finding the right balance between humans and machines in your organization.

Some of the things AIs and AI-enabled robots can do are pretty mind-blowing. For example, AIs can now read, write, see, speak and even understand emotions. While this sounds impressive, AIs are, for the most part, taking one type of input (be it visual data, written data, or whatever) and generating a particular output, as programmed. Once you understand this basic input-to-output idea, theres potential to automate all sorts of tasks that follow this same model, such as scanning security videos for suspicious behavior, moderating content online, answering simple customer inquiries, entering data, and maintaining bookkeeping records, and so on.

As Stanford professor Andrew NG puts it, If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future. In other words, human jobs that are built on some sort of input-to-output scenario are very likely to be automated in the future.

So what will happen to human workers?

In light of this incoming wave of automation, the work of humans will be affected in three key ways:

Displacement of human jobs. According to the World Economic Forums Future of Jobs Report 2020, 85 million jobs may be displaced by automation by 2025 truly a staggering figure. Naturally, this creates a lot of fear around automation. But while many jobs will be displaced, its important to note that even more jobs will be augmented or created because of technology adoption. Which brings us to

Augmentation of human jobs. Here, many jobs will be changed in some way by automation. According to the WEF, by 2025, the time spent on current tasks at work by humans and machines will be equal. This means employers must find the perfect balance between those tasks done by humans and those done by machines. To put it another way, we need to ensure the work given to machines is best suited to machines, and the work given to humans is best suited to humans (so humans dont end up feeling like machines).

Addition of new human jobs. Finally, new jobs will arise that previously did not exist. While the WEF estimates that 85 million jobs may be displaced, it also estimates that 97 million new roles may emerge roles that are better adapted to the new division of labor between humans and machines. These new human roles are likely to rely on a slightly different set of skills and capabilities, compared to those skills that have traditionally been prioritized in the past.

All this means employers have a responsibility to equip their workforces with the skills needed for the fourth industrial revolution. What sort of skills are we talking about? Well, with machines taking on more of the easily automated input-to-output work, its the inherently human skills that will become more and more valuable in the workplace. Things like empathy, creativity, critical thinking, emotional intelligence, communication, and complex decision making, to name just a few.

Responsible automation in practice

Stitch Fix is a fashion subscription box that uses AI to pick out clothes that customers will love. But the company doesnt just rely on AI to do this; its the perfect blend of AI and human stylists that makes the service so impressive.

At Stitch Fix, machines do the initial work of crunching through enormous amounts of data and evaluating the likelihood of a customer loving a particular style, based on the customers information, preferences and previous choices. Then a human stylist finalizes the selection and writes a personal note advising the client on how to style the items.

For me, this is a fantastic example of getting the best out of both machines and humans, and its something many organizations could learn from. This perfect symbiosis between intelligent machines and capable humans is referred to by automation pioneers Faethm as responsible automation. Faethm is on a mission to ensure automation is done in a way that doesnt leave humans behind, and the companys approach involves breaking jobs down into task fractions to see what can and cant be automated. Done this way, automation at least according to Faethm doesnt have to result in job losses. Instead, humans transition to more rewarding tasks.

The key takeaway here is that organizations must start to identify the tasks that are better suited to machines so that those tasks can be automated, leaving humans to do the more complex, rewarding work. And on top of this, employers must equip their workforces with the skills that will be essential for success in the 21st century.

To stay on top of the latest business and tech trends, subscribe to my newsletter and check out my books, Business Trends in Practice: The 25+ Trends That are Redefining Organizations, which has just won the Business Book of the Year 2022 award, and my new book Future Skills: The 20 skills and competencies everyone needs to succeed in a digital world. And of course, you can follow me on Twitter, LinkedIn, and YouTube and explore my website for more content.

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Human Vs. Artificial Intelligence: Why Finding The Right Balance Is Key To Success - Forbes

TURIN, Italy--(BUSINESS WIRE)--Today, cybersecurity represents an essential priority in the implementation of new technologies, especially given the crucial role that they have come to play in our private and professional lives. Smart Homes, Connected Cars, Delivery Robots: this evolution will not stop and so, in tandem, it will be necessary to develop automated and AI-based solutions to combat the growing number of security threats. The risks from these attacks are attributable to several factors, such as increasingly complex and widespread digital networks and a growing sensitivity to data privacy issues. These are the themes that emerge from the new Cybersecurity Automation research conducted by Reply, thanks to the proprietary SONAR platform and the support of PAC (Teknowlogy Group) in measuring the markets and projecting their growth.

In particular, the research estimates the principal market trends in security system automation, based on analysis of studies of the sector combined with evidence from Replys own customers. The data compares two different clusters of countries: the Europe-5 (Italy, Germany, France, the Netherlands, Belgium) and the Big-5 (USA, UK, Brazil, China, India) in order to understand how new AI solutions are implemented in the constantly evolving landscape of cybersecurity.

As cyberattacks like hacking, phishing, ransomware and malware have become more frequent and sophisticated, resulting in trillions of euros in damages for businesses both in terms of profit and brand reputation, the adoption of hyperautomation techniques has demonstrated how artificial intelligence and machine learning represent possible solutions. Furthermore, these technologies will need to be applied at every stage of protection, from software to infrastructure, and from devices to cloud computing.

Of the 300 billion in investments that the global cybersecurity market will make in the next five years, a large part will be directed toward automating security measures in order to improve detection and response times to threats in four different segments: Application security, Endpoint security, Data security and protection, Internet of Things security.

Application Security. Developers who first introduced the concept of security by design, an adaptive approach to technology design security, are now focusing on an even closer collaboration with the operations and security teams, termed DevSecOps. This newer model emphasizes the integration of security measures throughout the entire application development lifecycle. Automating testing at every step is crucial for decreasing the number of vulnerabilities in an application, and many testing and analysis tools are further integrating AI to increase their accuracy or capabilities. Investments in application security automation in the Europe-5 market are expected to see enormous growth, around seven times the current value, reaching 669 million euros by 2026. A similar growth is forecast in the Big-5 market, with investments rising to 3.5 billion euros.

Endpoint security. Endpoints, such as desktops, laptops, smartphones and servers, are sensitive elements and therefore possible sources of entry for cyberattacks if not adequately protected. In recent years, the average number of endpoints within a company has significantly increased, so identifying and adopting efficient and comprehensive protection tools is essential for survival. Endpoint detection and response (EDR) and Extended detection and response (XDR) are both tools created to accelerate the response time to emerging security threats, delegating repetitive and monotonous tasks to software that can manage them more efficiently. Investments in these tools are expected to increase in both the Europe-5 and Big-5 markets over the next few years, reaching 757 million euros and 3.65 billion euros respectively. There are also a multitude of other tools and systems dedicated to incident management that can be integrated at the enterprise level. For example, in Security Orchestration Automation and Response (SOAR) solutions, AI can be introduced in key areas such as threat management or incident response.

Data security and protection. Data security threats, also called data breaches, can cause significant damage to a business, resulting in risky legal complications or devaluating brand reputation. Ensuring that data is well-preserved and well-stored is an increasingly important challenge. It is easy to imagine how many different security threats can come from poor data manipulation, cyberattacks, untrustworthy employees, or even just from inexperienced technology users. Artificial intelligence is a tool for simplifying these data security procedures, from discovery to classification to remediation. Security automation is expected to reduce the cost of a data breach by playing an important role in various phases of a cyberattack, such as in data loss prevention tools (DLP), encryption, and tokenization. In an effort to better protect system security and data privacy, companies in the Europe-5 cluster are expected to invest 915 million euros in data security automation by 2026. The Big-5 market will quadruple its value, reaching 4.4 billion euros in the same timeframe.

Internet of Things security. The interconnected nature of IoT allows for every device in a network to be a potential weak point, meaning even a single vulnerability could be enough to shut down an entire infrastructure. By 2026, it is estimated that there will be 80 billion IoT devices on earth. The impressive range of abilities offered by IoT devices for different industries, though enabling smart factories, smart logistics, or smart speakers, prevents the creation of a standardized solution for IoT cybersecurity. As IoT networks reach fields ranging from healthcare to automotive, the risks only multiply. Therefore, IoT security is one of the most difficult challenges: the boundary between IT and OT (Operational Technology) must be overcome in order for IoT to unleash its full business value. As such, it is estimated that the IoT security automation market will exceed the 1-billion-euro mark in the Europe-5 cluster by 2026. In the Big-5 market, investments will reach a whopping 4.6 billion euros.

Filippo Rizzante, Replys CTO, has stated: The significant growth that we are witnessing in the cybersecurity sector is not driven by trend, but by necessity. Every day, cyberattacks hit public and private services, government and healthcare systems, causing enormous damage and costs; therefore, it is more urgent than ever to reconsider security strategies and reach new levels of maturity through automation, remembering that though artificial intelligence has increased the threat of the hacker, it is through taking advantage of AIs opportunities that cyberattacks can be prevented and countered.

The complete research is downloadable here. This new research is part of the Reply Market Research series, which includes the reports From Cloud to Edge, Industrial IoT: a reality check and Hybrid Work.

ReplyReply [EXM, STAR: REY, ISIN: IT0005282865] is specialized in the design and implementation of solutions based on new communication channels and digital media. Reply is a network of highly focused companies supporting key European industrial groups operating in the telecom and media, industry and services, banking, insurance and public administration sectors in the definition and development of business models enabled for the new paradigms of AI, cloud computing, digital media and the Internet of Things. Reply services include: Consulting, System Integration and Digital Services. http://www.reply.com

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Reply: Automation and Artificial Intelligence Are the Strategic Keys for an Effective Defense Against Growing Threats in the Digital World - Business...

Artificial intelligence (AI) is on the priority list for every executive who uses technology to enable their business. And today, every business is a technology business. Despite the excitement around AI and investments in its capabilities, only about a third of companiessay theyve adopted leading operational practices for AI but an increasing percentage are working toward that goal.

While AI is often seen as the golden ticket to take business operations into the 21st century and it can to do so, the technology must be approached specifically and strategically, not as an all-in-one solution.

In the universe of technology, one can picture a solar system of interdependent capabilities. At the core, cloud technology serves as the sun a central power source fuelling and enabling other technologies. Underlying cloud platforms, such as Amazon Web Services or Google Cloud, provide the basis for other capabilities to flourish in the technology universe.

Rotating around cloud platforms, there are various AI planets in orbit that build off of cloud infrastructure to deliver solutions such as automation, machine learning, robotic process automation, and more. Many business leaders are eager to enter the orbit of artificial intelligence solutions, but must first start by building the necessary foundation for successful AI implementations.

Once the centre of the AI solar system is in place, to effectively unlock the power of AI, its important that business leaders understand what it is they are trying to solve. And while many suppliers have powerful offerings, AI is not one-size-fits-all in its approach or implementation. It takes several capabilities and applications to drive true end-to-end AI outcomes.

This ecosystem strategy can ultimately offer flexibility and stability for IT decision-makers looking to harness business data and drive meaningful results for their organisations. Key to demonstrating the importance of AI ecosystems is discussing current barriers a company is trying to overcome and what specific AI capabilities will solve for them.

Today, business leaders are looking to define the function of artificial intelligence in their organisations and how they can effectively implement AI given their current technology stacks.

For example, a banking executive may look to automate some of their companys digital banking capabilities. To get there, the institution must consider how they are currently housing their data, how that data will be processed and then refined for usage, and finally how the data can provide insight to their workforce and what insights will be most valuable to them.

In this case, an organisation may have to consider combining the technology and environment they have in place with new technology and capabilities to achieve their desired outcome of a new automated banking tool. The allure of a one-stop shop for AI needs may sway businesses to heavily invest in one provider, which can put up roadblocks on the journey to a meaningful, AI-powered solution.

Part of the trouble with seeing one supplier as a silver-bullet solution is that businesses may invest too heavily in a provider that wont help them move the needle on all of their specific AI goals. Given the hefty budgets businesses are developing for their IT departments, its critical to understand that investments are going towards the appropriate solution(s) and that more money towards a nebulous, blanket AI may not always equate to unlocking business success.

IT decision-makers must have a clear understanding of their companys technology solar system before implementing a new AI tool Anthony Ciarlo and Frank Farrell, Deloitte

Moreover, the overarching cloud environment in which an AI solution is deployed can make or break its success. This means IT decision-makers must have a clear understanding of their companys technology solar system before implementing a new AI tool. When AI-related requests for proposal come across our desks, our first goal is to work through the specific needs of the clients organisation and if the resources they are putting behind the AI solutions will get them where they want to be.

End to end, it is difficult for any one supplier to meet all of the AI needs of an organisation. Some are leaders in automation, while others are leaders in data analytics or machine learning understanding these different strengths enables Deloitte to provide meaningful, tailored assessments as to what investments should be made.

As a systems integrator, once the Deloitte team has holistic insight into an organisations pain points, it can provide confident recommendations as to where money should be invested and how companies can see the greatest return on investment in their technology budgets. The Deloitte team delivers confidence in integrating and navigating the solar system to provide the desired outcomes its clients and their clients need.

The ecosystem approach to AI solutions marks an important shift for how systems integrators should be approaching their client solutions. In years to come, its likely that there will be increased collaboration across market providers, resulting in more streamlined, transparent AI implementation processes.

The key driver for this shift is continued conversations with business and technology leaders who understand that AI is not an isolated entity, but rather serves as a key component within a solar system of interconnected platforms and tools that can offer individualised solutions for the most pressing business challenges.

Anthony Ciarlo is strategy and analytics alliances leader and Frank Farrell is principal for cloud analytics and AI ecosystems at Deloitte.

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How to leverage the artificial intelligence solar system - ComputerWeekly.com

With the continued adoption of artificial intelligence in courts of law, can efficiency and effectiveness trump the concerns of legitimacy and justice?

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Academics and researchers gathered recently to discuss the findings of a new report on algorithms and their possibilities in the judicial system. Prepared and presented by DAKSH, a research centre that works on access to justice and judicial reforms, the report has been described as a superlative introduction to the various problems that ail our courts and how the usage of algorithms and allied technologies complicates it.

Artificial Intelligence (AI) systems have seen increased use in the Indian justice system, with the introduction of the Supreme Court Vidhik Anuvaad Software (SUVAS), used to translate judgments from English into other Indian languages, and the Supreme Court Portal for Assistance in Courts Efficiency (SUPACE), which would help the judges conduct legal research.

However, such systems are shrouded in secrecy as their rules, regulations, internal policy and functioning have not been properly documented or made available publicly. As such systems directly impact the efficiency and accessibility of the justice system in India, a framework that promotes accountability and transparency is warranted.

The new report examines the various domains of the judicial process where AI has been or can potentially be deployed, including predictive tools, risk assessment, dispute resolution, file management, and language recognition. It elaborates on the various ethical principles of regulating AI in the judicial space, and enumerates the challenges to regulation as observed in foreign jurisdictions. It also suggests several institutional mechanisms that would aid in regulating AI and making it a force for good.

The event was attended by the founder of Aapti Institute, Dr Sarayu Natarajan; associate professor in the Department of Humanities and Social Sciences at IIT-Delhi, Prof Naveen Thayyil; and the executive director at the Centre for Communication Governance at NLU-Delhi, Jhalak Kakkar. The event was moderated by senior research fellow at DAKSH, Sandhya PR.

Discussion traversed the domains of algorithmic accountability and the ethics of deploying such tools in a judicial system that seldom stays on an even keel.

Dr Natarajan praised the report for its comprehensive overview of the subject. She stated that the use of algorithms in availing judicial remedies should be understood with respect to various social categories such as caste, religion and economic backgrounds, which impact access. AI runs a chance of further alienating or marginalizing such social categories as far as access to justice is concerned.

Prof. Thayyil talked about his belief that AI will impact the coming two or three decades of the course of the Indian judiciary. This would escalate as the judicial system increases the use of such technologies in various facets of its functioning. As such, regulation of such technologies is crucial.

At present, no clear guidelines are available as to the control and effective management of AI and other tools in the justice system, and professionals would have to refer to the experience of other countries to adopt best practices.

To evaluate the desirability and degree of control that would be required, one needs to examine the impact of such technology in the real world. This concerns issues such as effectiveness, avoiding bias, ethical considerations, access issues, etc.

To measure such an impact, Prof Thayyil preferred the lens of regulatory ethics. He discussed his strong faith in the parameter of legitimacy, that is usually ignored during impact assessment of such tools, in favour of the more popular parameters of effectiveness and efficiency. He also stated that an ethics-based scrutiny of such systems would have to go beyond the procedure of such tools, and into the norms and values that inform them.

Notably, all panellists were cautious about stating which specific parts of the judicial process would be best optimised or were most likely to be experimented with, in the use of such technologies. They explained this by referring to the variety of parameters and access issues that have to be considered before deploying them.

The lack of public consensus and widespread distrust in AI would have to be factored with public consultations and reviews from industry experts.

Sandhya referred to the lack of explainability in many of the tasks touted to be accomplished. Explainability refers to features of AI systems which affect the capacity of humans to understand and trust the results of an AI system. Legitimacy, as such, is deeply impacted. Dr Natarajan expressed concern about the impact of technological intervention on the worst off among us.

Kakkar pointed out another challenge that complicates the deployment of such technologies, which is that they are usually developed by private parties and then enforced by the State. This makes it difficult to ensure accountability and transparency of the technologies.

AI systems are supposed to learn from the data fed to them, and this could perpetuate the discriminatory tendencies and practices already present within the judicial system. The need for transparency, she emphasized, was crucial, and this could be refined and adopted by subjecting the question to public scrutiny and expert audits.

Prof Thayyil resonated with the views presented and commented on the perception that the use of technology increases efficiency. Contrary to that, the reality may be that by reducing access and introducing bias, the efficiency may, in fact, decrease, he suggested.

There is also the argument of such technologization becoming the norm in the near future, which would make a return to a non-AI system difficult. The lack of transparency and accountability of such systems was addressed by Sandhya by referring to them as black boxes.

Developing policies on AI tools in India would have to go to the basics of an open justice framework, to make such technologies more coherent with the ends of justice being contemplated. Such a framework would necessitate the disclosure of the functioning and guidelines on the working of such technology and also subject them to effective control.

A cautious approach to such questions was reiterated with Kakkar stating that designing policies and managing data as means to regulation were inherently complex problems. The Indian experiment with regulation has been, so far, mixed, he suggested.

Since regulators function under legislation, the crucial question would be if it was too early or too late for a country like ours to have regulatory mechanisms for AI, in general.

If it is too early to have such a framework, the legislation would not be able to capture the nuances of the system that are yet to find use in the Indian justice system, but may eventually do. If it is too late for it, there is a chance that such regulation may be ineffective as the AI system has been irreversibly embedded in the way the judiciary functions.

The possibility and desirability of such a regulatory mechanism, and framing policies on the same, would depend on the goals sought to be achieved. For example, a target of enhanced security would necessitate an autonomous regulator with regulatory capacity to question both public institutions, which deploy such tools, and private institutions, which build them.

Kakkar reiterated Indias lack of a substantive data protection law, in which case the critical question is, what framework would be used to protect the fundamental and human rights of people whose data is being used by such systems. There are data gaps, as marginalized communities are generally neglected in building such technologies.

There is also the aforementioned possibility of the perpetuation of bias if such a regulatory mechanism is attempted by the courts themselves, in the absence of a regulatory legislation. Kakkar also agreed with Prof Thayyils anxiety about path dependencies, which suggests that the future course of AI depends on its deployment and percolation at present, and function creep, which suggests data may be used for other ends than demonstrated.

These issues may aggrandize these systems, expanding the scope of possibly harmful practices.

Dr Natarajan believed that if such a regulatory function was left to the courts, the myth of judicial exceptionalism would have to have sufficient heft to hold muster. To regular observers of the courts of law, it is obvious that such exceptionalism is hardly the norm, she observed.

As such, the judiciary cannot be solely trusted with such a regulatory task. Suggesting that it might be a little early to have a regulatory legislation for such technologies, Dr Natarajan affirmed her belief in the need for some basic regulatory mechanisms. These would examine the background of the developer of such technologies, prevent bias, among other things.

The panellists talked about regulation of similar tools in other domains, and the need to cull out a regulatory principle for AI which was more or less uniform across varied fields.

Best practices from different domains, such as healthcare, would have to be adapted because the ends of the two fields differ. This is because, while accuracy is the goal aimed to be achieved through such tech in healthcare, it is not the end but only a means to one in the case of law.

Similarly, adopting practices from other countries would have to take into account the resource settings of various jurisdictions, and a low resource country like ours would have to make certain adjustments before adopting practices from high resource jurisdictions such as China or Germany, it was felt.

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