Some industries will have to do significant machine learning adoption.

Gradually recovering from the effects of COVID-19 pandemic, will be a top priority for practically every firm and industry in 2021. A few organizations may get stale or never recuperate. Others will see the purge as a remarkable opportunity to comprehend and improve their data and analytical assets, operationalize and update their model production process, and promise clients that their machine learning adoption can be trusted. Everybody is hoping to improve over their present AI and ML insights, for example, a bank improving fraud detection, a medical care provider moving to telehealth, a retailer or manufacturer attempting to make your supply chain more proficient.

All through the recent years, there have been a couple of revelations in machine learning and artificial intelligence. Several companies have so far been able to apply those to achieve the fundamental business targets.

With the rising demand of ML and interest in these advances, different ML trends in 2021 are climbing. Basically, in case you’re a tech able or related to innovation in some capacity, it’s overwhelming to see what’s next inside in ML for business. 2021 will see more machine learning adoption in industries that are fundamental to the functioning of society as a whole.

Banking

As the world starts its recuperation from the pandemic in 2021, sensational swings will happen across the macro-economy. A significant topic will be the impacts of fiscal stimulus and the reverberations that will be felt by families and bigger organizations. Banks and other financial institutions will be searching for both generous opportunities and huge threats, and the persistent suppression of interest rates will be a significant challenge as compressed spreads will burden profitability.

Utilizing obsolete models of machine learning will make banks quickly lose profit, market share of the overall industry and, now and again, reputation. Hence, the skill to quickly update models in sectors, for example, fraud, underwriting, customer management, etc., will be crucial.

Healthcare services

The worldwide pandemic has underscored the significance of investing in and streamlining our healthcare systems. ML for business is viewed as the most encouraging technology that permits healthcare suppliers to beat the enormous volumes of data and infer important clinical insights. ML and AI offer remarkable advancement in drug discovery, chopping down the long discovery and development pipeline and lessening cost. It can likewise altogether improve healthcare delivery systems and thus lift the overall quality of medical care while controlling cost. One of the ML trends in 2021 is that it can be used in clinical trials also. Machine learning will immensely affect almost all parts of medical care including pharma and biotech, experts underline.

Retail

The retail business is in crisis, yet there is a lot of strength and opportunity, and the retail landscape will keep on seeing sensational trends in consumer behavior. A few unsure variables will keep on challenging the business in 2021: jobs, the economy, and the logistics of facilitating pandemic restrictions in individual districts. Retailers will be compelled to do machine learning adoption for their business decisions, particularly to comprehend the always changing, underlying data. MLOps will be a key ML trend in 2021 in retail to operationalize the model update process, identify changes in economic and consumer data, and comprehend the significance of those changes.

Manufacturing

With the monstrous adoption of IoT devices set to additionally grow in the manufacturing industry, machine learning will be the most crucial technology that analyzes the enormous volumes of data produced. ML for business fills in as the incredible building block of Industry 4.0 alongside automation and data connectivity. While predictive maintenance is the most common use case up until this point, manufacturers will see more developed use cases of ML like supply chain visibility, cost reduction, real-time error detection, warehousing efficiency, and asset tracking among others. As traditional manufacturing plants shift to smart factories, ML will fuel more noteworthy advancement and productivity in the days to come.

Transportation

If you think self-driving vehicles are the results of a distant future,  smart cars have effectively penetrated into the markets. Back in 2015, the execution of AI-driven systems in cars and vehicles were simply 8%, yet by 2025, the rates are expected to leap to 109%. Connected vehicles are the in-thing in the automobile business at the present time, where predictive mechanisms precisely tell drivers the likely breaking down of spare parts, routes and driving directions, emergency crisis and disaster prevention protocols and much more. Gartner anticipated that connected cars with embedded wireless connectivity and networks would be the benchmarks for vehicles by 2021. This is likewise gradually transforming into a reality with the prototypes of autonomous cars hitting the streets.

The post MACHINE LEARNING ADOPTION WILL INFLUENCE THESE FIVE INDUSTRIES appeared first on Artificial Intelligence.

AI can exploit the vulnerabilities of a person’s decision-making habits and patterns.

A new study by researchers from the Commonwealth Scientific and Industrial Research Organisation’s (CSIRO) Data61, in partnership with the Australian National University and researchers from Germany, has revealed that artificial intelligence (AI) can influence human decision-making.

Spearheaded by CSIRO scientist Amir Dezfouli, the study [PDF] involved running three experiments where participants played games against a computer.

The first two tests involved participants clicking on red or blue coloured boxes to win a fake currency. In the third experiment, participants were given two options as to how they could invest some fake currency. In the scenario, participants played the role of the investor while the AI played the role of the trustee.

As all three games went on, the AI learned the participants’ choice patterns that eventually saw it guide the players towards specific choices. For instance, by the third game, the AI was learning how to get participants to give it more money.  

Dezfouli said the study highlighted that AI could influence human decision-making by exploiting the vulnerabilities of an individual’s habits and patterns.

“Although the research was theoretical, it advances our understanding of how people make choices. This knowledge is valuable because it allows us to mitigate our vulnerabilities so we can better detect and avoid flawed choice as a result of potential misuse of AI,” he said.

He added that the way future AI operates will be dependent on its creators.

“Ensuring AI and machine learning are used as a force for good — to improve outcomes for society — will ultimately come down to how responsibly we set them up in the first place,” Dezfouli said.

The study has been published in the Proceedings of the National Academy of Sciences journal.

At the end of last year, Data61 researchers developed an implantable artificial intelligence monitoring and seizure detection helmet system designed to prevent seizure disorders for patients who have undergone decompressive brain surgery.

The detection system was developed and trained using traumatic brain injury data from Monash University to monitor brain activity for seizures while in standby mode before it is reactivated when a seizure is detected.

“Monitoring brain activity post-surgery is especially critical to a patient’s recovery as seizures can regularly occur, often leading to patients developing epilepsy,” CSIRO’s Data61 researcher Dr Umut Guvenc said at the time.

“These seizures are often difficult to detect, with current monitoring techniques only able to be used in a hospital using bulky devices for less than 24 hours, providing a brief snapshot of brain activity during that time only.

“This new method can continuously monitor brain activity wirelessly, allowing the patient to be mobile, comfortable, and more socially active.”

The post Artificial intelligence can influence human decision-making, new Data61 study reveals appeared first on Artificial Intelligence.

Key Takeaways

• Since 2018, several new microservices frameworks – including Micronaut, Helidon and Quarkus – have been introduced to the Java community, and have made an impact on microservices-based and cloud-native applications development.
• The MicroProfile community and specification was created to enable the more effective delivery of microservices by enterprise Java developers. This effort has influenced how developers are currently designing and building applications.
• MicroProfile will continue to evolve with changes to its current APIs and most likely the creation of new APIs.
• Developers should familiarize themselves with Heroku’s “Twelve-Factor App,” a set of guiding principles that can be applied with any language or framework in order to create cloud-ready applications.
• When it comes to the decision to build an application using either a microservices or monolithic style, developers should analyze the business requirements and technical context before choosing the tools and architectures to use.

In mid-2016, two new initiatives, MicroProfile and the Java EE Guardians (now the Jakarta EE Ambassadors), had formed as a direct response to Oracle having stagnated their efforts with the release of Java EE 8. The Java community felt that enterprise Java had fallen behind with the emergence of web services technologies for building microservices-based applications.

Introduced at Red Hat’s DevNation conference on June 27, 2016, the MicroProfile initiative was created as a collaboration of vendors – IBM, Red Hat, Tomitribe, Payara – to deliver microservices for enterprise Java. The release of MicroProfile 1.0, announced at JavaOne 2016, consisted of three JSR-based APIs considered minimal for creating microservices: JSR-346 – Contexts and Dependency Injection (CDI); JSR-353 – Java API for JSON Processing (JSON-P); and JSR-339 – Java API for RESTful Web Services (JAX-RS).

By the time MicroProfile 1.3 was released in February 2018, eight community-based APIs, complementing the original three JSR-based APIs, were created for building more robust microservices-based applications. A fourth JSR-based API, JSR-367 – Java API for JSON Binding (JSON-B), was added with the release of MicroProfile 2.0.

Originally scheduled for a June 2020 release, MicroProfile 4.0 was delayed so that the MicroProfile Working Group could be established as mandated by the Eclipse Foundation. The working group defines the MicroProfile Specification Process and a formal Steering Committee composed of organizations and Java User Groups (JUGs), namely Atlanta JUG, IBM, Jelastic, Red Hat and Tomitribe. Other organizations and JUGs are expected to join in 2021. The MicroProfile Working Group was able to release MicroProfile 4.0 on December 23, 2020 featuring updates to all 12 core APIs and alignment with Jakarta EE 8.

The founding vendors of MicroProfile offered their own microservices frameworks, namely Open Liberty (IBM), WildFly Swarm/Thorntail (Red Hat), TomEE (Tomitribe) and Payara Micro (Payara), that ultimately supported the MicroProfile initiative.

In mid-2018, Red Hat renamed WildFly Swarm, an extension of Red Hat’s core application server, WildFly, to Thorntail to provide their microservices framework with its own identity. However, less than a year later, Red Hat released Quarkus, a “Kubernetes Native Java stack tailored for OpenJDK HotSpot and GraalVM, crafted from the best-of-breed Java libraries and standards.” Dubbed “Supersonic Subatomic Java,” Quarkus quickly gained popularity in the Java community to the point that Red Hat announced Thorntail’s end-of-life in July 2020. Quarkus joined the relatively new frameworks, Micronaut and Helidon, that were introduced to the Java community less than a year earlier. With the exception of Micronaut, all of these microservices-based frameworks support the MicroProfile initiative.

The core topics for this virtual panel are threefold: first, to discuss how microservices frameworks and building cloud-native applications have been influenced by the MicroProfile initiative. Second, to explore the approaches to developing cloud-native applications with microservices and monoliths, and also the recent trend in reverting back to monolith-based application development. And third, to debate several best practices for building microservices-based and cloud-native applications.

The post Virtual Panel: The MicroProfile Influence on Microservices Frameworks appeared first on Artificial Intelligence.

The world has gone digital. In fact, we are approaching the Fourth Industrial Revolution, also described as Industry 4.0: an age in which a range of new technologies is expectedly fusing the physical, digital and biological worlds in addition to impacting all disciplines, economies and industries.

This is an age marked by emerging technology breakthroughs in a number of fields, including artificial intelligence, robotics, augmented reality, nanotechnology, quantum computing, biotechnology, The Internet of Things, 3D printing and driver-less vehicles, among others.

One of these emerging technologies, Artificial Intelligence or AI, as it is commonly referred to, has the potential to cause significant disruptions to many established industries, presenting amazing new ways for business leaders and individuals to simplify complex tasks.

According to the research and development unit of Yudala, a Nigerian e-commerce outfit, here are six ways AI will shake up things in the corporate world:

1. Double-edged impact on jobs and employment opportunities: Artificial intelligence promotes the growing reliance on machines, which possess the ability to perform a wide range of physical and cognitive tasks with more efficiency and accuracy. This poses a great deal of concern for the future as these machines may put many jobs at risk and ultimately reduce human employment. While these concerns are legitimate, research posits that the rise of AI and automation may, nevertheless, have a double-edged impact on employment: negative and positive. In the negative sense, AI may displace humans by directly replacing them in tasks previously performed by these workers. On the positive side, the deployment of machine learning and AI may end up increasing the demand for labour in other industries or create new jobs or openings as a result of automation.
2. Data analysis and presentation: With the emergence of machine learning, which is a direct off-shoot of AI, the task of crunching numbers, analysing data and presenting this as usable information just got easier. A single supercomputer using AI and running on continuous machine learning will definitely out-perform the work of 10 or more humans working with spreadsheets to analyse and interpret the same amount of data. Most machine learning apps have the capability of, not only analysing huge amounts of data in record times, but also reducing errors to the barest minimum – a factor that makes them logically preferable to high-cost and error-prone human accounting or consultancy teams.
3. Budgeting: Closely related to the foregoing is the refinements and research-backed approach to the process of budgeting which AI lends. For most businesses, budgeting is often carried out haphazardly on a yearly basis, with funds allocated in similar patterns to various activities and business units. This inadvertently results in a situation where certain business units get more working capital while other units that yield more returns struggle with the meagre figures allocated to them. By deploying AI, business leaders can generate valid information on the performances of various business units and gauge more accurately the returns on the investments made on specific activities. This will potentially cause a major overhaul of existing budgeting systems used by corporate organisations and bring more value and objectivity to the entire process.
4. More personalised marketing: The exciting benefits of artificial intelligence will also be keenly felt in the field of marketing, as advances in the field will usher in landmark advances in the marketing value chain, leading to more personalised experiences for consumers. Through AI tools, marketing will assume an automated dimension, enabling companies to deliver a richer, more personalised experience for all classes of consumers and resulting in stronger connections between brands and consumers. Ultimately, these new approaches will build brand affinity/loyalty and positively impact bottom lines. Presently, a number of organisations are deploying AI principles to simplify the processes involved in various branches of marketing including email marketing, search engine optimisation, mobile, and social media, among others. Global e-commerce giant, Amazon, is playing a leading role in this aspect by relying on a series of algorithms to determine what customers are likely to buy or require next, with these suggestions and creatives sent to the target customers via customised emails in a process that is entirely automated.
5. Recruitment/human resources: AI will radically transform the recruitment and human capital management function by infusing more objectivity and transparency into the entire process. This will equip business leaders or owners with the requisite insight to take more informed decision on hiring, compensation, promotion and gender pay disparity/biases in the work-place. By leveraging the benefits of advanced statistical tools and machine learning, AI can help organisations recognise and reward performance more objectively among the entire workforce through the detection and elimination of partiality, imbalances and subjective appraisals.
6. Banking and finance: The entire financial services sector will also be disrupted by the emergence of AI solutions and applications. A recent research survey carried out by The Economist revealed that 49 per cent of banking executives believed the traditional transactional banking model would be dead by 2020. Through AI, banks, insurance companies, asset/wealth management firms and capital markets would have a plethora of cutting-edge tools that would streamline and bring unimaginable simplicity and ease to their core financial processes and customer service offerings. The potential is limitless. Robo-advisors are being mooted in the sphere of asset management; banks will expectedly rely on chatbots to boost customer experience. AI will also help insurance firms process claims, streamline their processes and uncover fraud. Understandably, a number of financial institutions are awake to this reality, head-hunting and mopping up fintech talents, in a sort of arms race to ensure they are not left behind in the sweeping wave of tech-mediated changes that is afoot in the sector.

#Takeaway

What is artificial intelligence?

Artificial intelligenc, also known as machine intelligence, is intelligence exhibited by machines, rather than humans or other animals (natural intelligence). In computer science, the field of AI research defines itself as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximise its chance of success at some goal. Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”. AI research is divided into subfields that focus on specific problems, approaches, the use of a particular tool, or towards satisfying particular applications. Artificial intelligence is not one technology but rather a group of related technologies – including natural language processing (improving interactions between computers and human or “natural” languages); machine learning (computer programs that can “learn” when exposed to new data) and expert systems (software programmed to provide advice) – that help machines sense, comprehend and act in ways similar to the human brain. These technologies are behind innovations such as virtual agents (computer-generated, animated characters serving as online customer service representatives); identity analytics (solutions combining big data and advanced analytics to help manage user access and certification) and recommendation systems (algorithms helping match users and providers of goods and services) which have already transformed the ways in which companies look at the overall customer experience.

The post Six ways artificial intelligence will influence the world appeared first on Artificial Intelligence.