• The telecoms industry needs machine learning to be able to process and regain control over what’s done with available data.
• The technology’s use cases in telecoms have shown great potential in assisting with anomaly detection, root cause analysis, managed services, and network optimization.

As technologies like artificial intelligence (AI) and machine learning (ML) become ubiquitous, it will be almost impossible to come across any industry not capitalizing on the benefits they can provide. The telecoms industry has traditionally navigated quite well through tech change.  Globally, they managed to transform from landline to mobile carriers, then move from voice calls to messaging and data-centric networks. In most of the developed markets, telecoms are creating ecosystems for the data-driven economy.

The reality on ground is that the telecoms industry is one of the fastest-growing industries as well as one that uses AI and ML in many aspects of their business from enhancing the customer experience to predictive maintenance to improving network reliability. ML in particular has numerous potential use cases since telecommunications companies deal with vast amounts of data and need to drive conclusions from it – an overwhelmingly difficult proposition to do manually. 

According to Ericsson in a blog post, “In the area of system monitoring, anomaly detection systems are crucial for identifying performance issues and problematic network behavior. Proactively predicting the degradation of key performance indicators, and identifying the likely root cause, can help reduce and prevent outages.”

As for the area of managed services, Ericsson said ML models can improve trouble ticket management by effectively classifying, prioritizing, and escalating incidents. Capacity planning and customer retention can be improved through explainable churn prediction.  “Furthermore, in the area of intelligent networks, the incorporation of ML tools can enable self-healing radio networks, which automatically detect issues and take corrective actions,” the report said, adding that new technologies such as deep learning and reinforcement learning can be used to automate the network design process and optimize network performance in real time.  

Common ML system components & use cases

Data is the lifeline of any ML system and telecoms data is complex, multimodal, and plentiful. It comprises numerical metrics and text-based logs collected from many thousands of devices. The computational and communication costs of processing the data, as well as the latency and performance requirements, determines how the data components should be designed and implemented. 

Another use case would be the offline and online predictions. ML predictions can be made in either periodically scheduled batches (offline), or in a dynamic streaming manner in real time (online) and batch prediction may be suitable when some delay is acceptable. In batch prediction, model prediction requests are accumulated over time, and the model responds to each batch of requests at an appropriate, predetermined time. 

The report stated that for mission-critical tasks such as predicting service outages, however, real-time predictions may be required. In this mode of operation, the ML model service immediately returns a prediction output upon receiving input data. This execution mode can have challenging requirements from an operational standpoint because real-time prediction may need to support a large and unpredictable number of requests, the model service may need to scale dynamically and provision more resources at peak request times. 

Then there’s the workflow management use case as well whereby to orchestrate the entire end-to-end ML pipeline, workflow management tools can help immensely. “An ML pipeline consists of a number of inter-dependent tasks including data collection, transformation, validation, training, and serving. Workflow management tools can help effectively chain these tasks together, such that unexpected delays or issues in one step do not break subsequent steps,” Ericsson said. 

While ML solutions are complex systems composed of several components that may differ from the existing infrastructure organizations have in place, the report said, depending on the particular use case, each of these sub-components may be implemented in a different manner.

The post The case for machine learning transforming the telecoms industry appeared first on Artificial Intelligence.

Today, AI and ML are transforming the face of education technology.

Today, AI and ML (Artificial Intelligence, Machine Learning) are creating havoc in a number of fields and industries, including education. As per report of Ideamotive, according to Market Research Engine, the global AI in the education market will reach $5.80 billion by 2025 at a compound annual growth rate of 45%.

The following are some of the ways that machine learning and artificial intelligence are transforming the face of education technology:

An Approach to Learning That Is Both Structured and Personalized

The use of machine learning and artificial intelligence has assisted in the development of a more effective and time-saving approach for teachers to follow a clear ride of guidance. This has aided students’ comprehension and has gone beyond the criteria of human intelligence. Many ed-tech companies have started to deploy digital programs to boost the educational experiences, as AI in ed-tech helps respond to students’ specific needs. It helps students in assessing their success on various subjects and keeps track of their input.

Augmented and Virtual Reality

It is one of the most exciting advances in the area of AI and machine learning. Many colleges and universities are using this advanced technology to clarify life-like experiences in disciplines such as biology, astronomy, geology, and others. Students are able to interact with different topics using AR/VR technologies that include animations, pictures, movies, and more. This technology has proven to be the most effective means of assisting teachers and administrators in obtaining extremely accurate subject-oriented experiences.

Choosing the Best Profession

AI and machine learning will assist students in overcoming their dilemmas and predicaments when it comes to choosing the right career direction. Many times, poor decisions on this crucial front have resulted in the futures of millions of talented students being jeopardized. Fortunately, in the future, AI and machine learning will be able to save students from the regrettable pain of self-inflicted career destruction. All of these tools have excellent data mining techniques, which inevitably provide deep insight into students’ interests and despises, as well as their long-term objectives.

For Students with Special Needs, AI and Machine Learning is a Boon

AI and machine learning technology have proved to be an outstanding source of education for students with special needs. Many specially-abled students are encouraged to learn the subject through speech recognition and virtual reality technology, which enable them to effectively and ideally master even the most difficult topics.

What role does AI play in education?

AI is now being used in education, especially in the form of skill development tools and testing methods. When AI educational solutions develop, it is anticipated that AI will be able to help identify voids in teaching and learning, allowing teachers and administrators to do better than ever before.

There are several AI applications in education. Both teachers and students profit from the innovation. The education industry will benefit from AI in a number of ways. Byju’s, Vedantu like e-learning sites, in particular, are offering educational institutions a competitive advantage. It has introduced artificial intelligence (AI) e-learning software to connect with students and have more customized tutorials.

Conclusion

Education will be rapidly accelerated in the future, and learning needs will be even more diverse. Artificial intelligence can be extremely useful in identifying patterns before they take root and rapidly adapting to them.

The curriculum of the future educational institutions will be able to adjust as required. Additional teaching technologies will help students without putting undue pressure on teachers, and educators will be able to use their time and efforts more dynamically.

The post AI AND ML TO FURTHER REVAMP THE ED-TECH SECTOR? HERE’S HOW! appeared first on Artificial Intelligence.

Using Machine learning and Artificial Intelligence along these lines, banks get a clear image of risks and danger and possible return for every individual, prompting more secure choices and fewer people defaulting on their credits.

For a long time, banks have been at the leading edge of utilizing innovation to assist with front-end and back-end activities. It’s nothing unexpected that banks are using artificial intelligence and machine learning techniques to help in a plethora of ways. These emerging technologies are way too useful than one can imagine.

Digital transformation is incredibly essential given the extraordinary occasions we are in. To modernize banks and heritage business frameworks and policies without interrupting the current framework is one of the significant difficulties. Artificial Intelligence and ML techniques are an excellent way to deal with framework modernization that will permit organizations to work together with other FinTech administrations.

Benefits of AI and ML in the Banking sector

Artificial intelligence and Machine Learning in the banking sector will forever shape how banks work and perform their duties. Unavoidably, they will help both the bank and the client have a more exhaustive and gainful experience. Specialists anticipate that machine learning and AI in banking will have major essential effects. The banking sector extensively uses AI and ML to automate processes and make them easier. A few major use-cases where these emerging technologies used are:

● AI and ML for fraud detection:

Theft, fraud, and security penetrate the banking area because of the sensitive information and cash. Information security is fundamental to an effective bank and keeping up client trust.

Renowned banks are on the curve regarding embracing artificial intelligence and machine learning as a business technique – a fundamental undertaking for any significant association looking for an edge over their rivals. With a particularly massive and conveyed client base, the bank needs to keep on developing to best help their clients. They are doing this with artificial intelligence to improve the items and contributions for their client.

Usually, associations use artificial intelligence and banking to rapidly identify extortion without the danger of human mistakes, disregarding any information or misconception designs.

● Customer service

Client support is a fundamental part of banking and frequently has the greatest effect wherein a bank a forthcoming client picks. It’s obvious then that this is a zone where banks are testing the most with artificial intelligence in banking to upgrade client connections and improve the general client bank communication. Conversational artificial intelligence and machine learning are now changing financial client support by accommodating chatbots, feedback, and many more, which give a more customized satisfaction on the web and versatile financial experience for the client.

Virtual assistants such as Alexa, Siri, Cortana, and so on, upheld by AI, utilize prescient investigation to decide the correct pathways to coordinate clients and smooth the way toward drawing in with the bank. Clients can interface with these artificial intelligence banking bots through messaging or tapping through orders on their screens.

● Credit service and loan decisions

Using Machine learning and Artificial Intelligence along these lines, banks get a clear image of risks and danger and possible return for every individual, prompting more secure choices and fewer people defaulting on their credits. Credit service and loan decisions with advance choices have verifiably been made by investigating financial assessments, records, and other past practices. This is nothing but a precise science, and banks frequently lose cash due to having incorrect information. AI and Ml are used to investigate elective information in advance, and credit score will raise some protection, moral, and legitimate concerns for every individual through their respective banks.

Banking sectors with these two technologies may very well make a conceivable pardon give credit to the individuals who are in terrible danger. Accomplishing a portion of these new businesses could probably prompt other less circumspect passages into the market.

● Meets regulatory compliance

With artificial intelligence’s capacity and machine learning modes, banking is more likely to identify extortion through continuous investigation and incorporation with network safety frameworks. As of now, banks are, perhaps the most profoundly directed foundations worldwide and should conform to exacting government guidelines to forestall defaulting or not getting monetary violations inside their frameworks and policies. On top of examining client conduct, artificial intelligence and machine learning in banking can log key examples and other data for answering administrative frameworks, which means less human information section is required. As AI and ML in banking are utilized all the more, we hope to see monetary guidelines develop with these changes.

Toward the end, it’s essential to ensure organizations that find harmony between minimizing expenses for their individuals while permitting the organization to push ahead through Artificial Intelligence and Machine Learning innovations to improve and give superb client assistance and incredible client items for their individuals. The appropriation of these emerging technologies in the banking sector is proceeding to change organizations in the business, give more noteworthy degrees of significant worth and more customized encounters to their clients, decrease dangers, and increment openings engaged with being the monetary motors of our advanced economy.

The post How AI And Machine Learning Are Transforming The Banking Industry appeared first on Artificial Intelligence.

If ever there has been a need to adapt, 2020 has let it be known that this is the year. COVID-19 has brought about all kinds of workplace changes, from remote work with video meetings to temporary layoffs. It may seem like just another buzzword, but microservices can help human capital management (HCM) systems face the multitude of challenges that 2020 brings and help ensure workforce resiliency.

What microservices are

Darshan Kapadia, director of platform engineering at Lifion by ADP, explains that microservices are a way of designing and developing software so that each of the individual components can be worked on and deployed independently of the others. The key here is components are self-contained. This architecture allows for a rapid software release cycle because one component team doesn’t need to wait for another to finish before deploying.

Compare this model to more traditional monolithic architecture, which is still used widely: The product is developed as a whole by one large team and released as one large product. That means all the smaller teams working on components need to coordinate and deploy the product simultaneously.

Why microservices work so well

There are two reasons Kapadia touches on that explain why microservices have an edge.

1. Releasing a new product in small pieces that can run independently, instead of one large product, allows teams to focus on their own smaller piece of code. There’s a lower chance of failure when a new product is deployed, “and the blast radius is small,” he says. In addition, bug fixes and performance updates can be deployed as they are ready on an individual basis vs having to sustain a longer cycle between updates.
2. With constant innovations in technology, microservices enable organizations to add new components without waiting for a complete overhaul of an outdated system. Kapadia explains how at Lifion, they went from self-managed databases to entirely genuine in just under nine months. “That is unimaginable if you are in a traditional monolithic system with multiple databases,” he says. “It isn’t possible because you have to coordinate everything before that can happen,” something that can take a year’s depending on system complexity.

Moreover, the research and improvements happening in the UX space are significant right now, and in order to get those tools into the hands of customers, UI needs to be a separate component that can be deployed independently.

Microservices also employ simple routing methods, receiving requests, processing those requests, and then responding accordingly. This is in contrast to architecture involving enterprise service buses which use more complex integration approaches.

The HCM pivot during COVID-19

Most, if not all, organizations have needed to pivot as the pandemic changed the way we work. In the case of HR products, there have been changes in taxation and new payroll terms added as well as return-to-work protocols.

Microservices can expedite these types of changes, making them easier to deploy than a traditional product, since the tech team is able to work continuously behind the scenes, releasing updates as they are ready.

Not only that, with everyone working from home for months at a time communication can become a problem no matter how many video calls are scheduled. With microservice architecture, however, “communication doesn’t become a barrier,” explains Kapadia. “That’s because the microservices are talking to each other with a very strict set of contracts that can’t be broken.” This allows team members to perform updates behind the scenes with minimal communication, he says.

Aside from the presence of the global health event, what makes microservices so valuable to HCM is the ability to mitigate costs by scaling services as needed and independently of others. Spikes in traffic for specific systems, such as payroll, time, or benefits, come at predictable times, Kapadia says, so scaling each one individually rather than altogether for every single one of those times can save money.

What you should have first

Kapadia explains that there are some circumstances in which a company won’t benefit from microservices. An organization should be completely on board with the microservice paradigm, since services will be divided into small chunks for different teams. All teams should be in agreement, he says, because while each piece is developed independently, they need to all work together. It’s also critical to ensure that the boundaries of microservices are well defined. If a large number of microservices need to communicate for performing a single task, it can cause performance issues due to network latency between services.

In addition, it’s important to have a robust Continuous Integration and Continuous Deployment (CI/CD) pipeline in place because developing, testing, and deploying products will take place independently. The right tools and platforms should also already be in place, with active support for flexible runtime deployment and automated monitoring, as well as container hot deployment.

Organizations need to be agile and willing to adapt to a larger variety of scenarios than ever. The ability to pivot and adapt quickly is exactly what microservice architecture is designed for.

The post How Microservice Architecture Is Transforming Human Capital Management appeared first on Artificial Intelligence.

We are living in a defining moment in history when businesses need a reformed approach—with emerging technology maturing and consumers expecting a faster pace of delivery—teams are overworked, and agility has become a mandatory requirement. As a result, the role of finance and accounting is evolving to support these tremendous changes.

In 2019, the IBM Institute for Business Value published the report “The enterprise guide to closing the skills gap” in which it indicated a staggering “120 million workers in the world’s 12 largest economies might need to be retrained/reskilled in the next three years as a result of intelligent/AI-enabled automation.”

As CFOs implement plans to prepare their teams for the future, finance and accounting professionals are under pressure to enhance their value offering and reduce costs while acquiring new skills.

Emerging digital technologies provide the finance and accounting function with a path to fulfill these objectives while meeting business demand for advanced analytics, efficient operations, and strategic decision support.

Robotic process automation

Robotic process automation (RPA) presents a clear and sustainable avenue to transforming the finance function.

Although several digital tools can be leveraged to automate finance and accounting processes, RPA is currently recognised as one of the few emerging technologies capable of automating a significant amount of finance and accounting end-to-end processes.

The importance

In a recent RPA webinar hosted by IMA, attended by nearly 1,500 finance and accounting professionals from all around the world, 34 percent of participants acknowledged RPA would be the emerging technology with the most significant impact on the profession in the next three years.

In India, RPA is bound to create new sets of job opportunities for people.

According to a recent report, the RPA market in India will grow at a CAGR of above 20 percent during the forecast period of 2019-2025.

The report says the RPA market in the country is driven by the increasing demand for automated accounting and process management.

Further, to ensure automated transaction processing improves over time, RPA vendors are also focusing on developing best-in-class intelligent process automation bots that learn as they work.

Businesses that have incorporated finance and accounting professionals into their RPA program have reaped the benefits of more robust automation solutions, less costly implementations, and improved employee satisfaction.

Unlike what some might think, RPA at scale—or fully-leveraged—could be a perfect solution for a small or mid-sized business with overworked finance and accounting teams needing relief and leaders seeking to elevate their limited resources’ offering.

By implementing RPA, start-ups can reassign their teams to more pressing matters once their schedules have been cleared of repetitive work. It could equally serve as a monumentally transformational initiative in larger enterprises where opportunities in other parts of the organisation may be brought to light.

Specific to finance and accounting departments, team members who learn of this technology, proactively train staff on RPA, and/or lead RPA programs, tend to gain more benefits, both professionally and organizationally, than those on the receiving end of automation solutions.

Organizations with finance and accounting functions that are equipped with business professionals who are cross-functionally trained find themselves far ahead of their peers with more time to focus on higher value-added tasks.

The historical nature of the finance and accounting function’s role dictates that many of its processes are repetitive and rule-based—two of the most critical criteria in identifying good RPA candidates. Therefore, it is not surprising that most RPA implementations begin in the finance and accounting department.

The Impact

As RPA is an emerging technology with one of the lowest barriers to entry, the impact of RPA on the finance and accounting function is two-fold:
Finance and accounting processes will be automated with RPA
Finance and accounting professionals can upskill with RPA
Misconceptions about RPA technology cross several extremes—from “It will automate all of our jobs” and “Only IT can implement it” to “RPA couldn’t possibly do what I do” and “RPA has no applicability to finance and accounting processes.”

Each of these misconceptions can be dispelled through knowledge of what RPA is and the actual capability of the technology.

Most RPA software are made up of three primary components: the bots, a bot manager, and a workflow design module.

The bots perform processes, the bot manager enables scheduling and allocation of developed processes, and the workflow design module is where processes are developed.

Although it is tempting to say—and is widely said—during an RPA implementation, people do not create bots. The truth is, they develop the processes that bots will perform.

In organizations that have made progress along the RPA journey, they operate in an environment where finance and accounting professionals work alongside human and digital co-workers. They receive data from bots and supply inputs to them for processing. This is a different world—the technology to make this a reality already exists and is currently in place in many enterprises.

Winding-up

As RPA vendors strengthen their native offerings and progress with integrating technology partnerships, the complexity of the processes digital teammates can perform with intelligent RPA will undoubtedly increase. And even though widespread democratisation of RPAis the concept of a bot for every employee, may still be far off, digital teammates are already on the payroll and leaders are gladly assigning them finance and accounting tasks.

The post Robotic Process Automation: Transforming the world of finance appeared first on Artificial Intelligence.

Artificial intelligence and machine learning technologies are poised to supercharge productivity in the knowledge economy, transforming the future of work.

But they’re far from perfect.

Machine learning (ML) – technology in which algorithms “learn” from existing patterns in data to conduct statistically driven predictions and facilitate decisions – has been found in multiple contexts to reveal bias. Remember when Amazon.com came under fire for a hiring algorithm that revealed gender and racial bias? Such biases often result from slanted training data or skewed algorithms.

And in other business contexts, there’s another potential source of bias. It comes when outside individuals stand to benefit from bias predictions, and work to strategically alter the inputs. In other words, they’re gaming the ML systems.

It happens. A couple of the most common contexts are perhaps job applicants and people making a claim against their insurance.

ML algorithms are built for these contexts. They can review resumes way faster than any recruiter can, and can comb through insurance claims faster than any human processor.

But people who submit resumes and insurance claims have a strategic interest in getting positive outcomes – and some of them know how to outthink the algorithm.

This had researchers at the University of Maryland’s Robert H. Smith School of Business wondering, “Can ML correct for such strategic behavior?”

In new research, Maryland Smith’s Rajshree Agarwal and Evan Starr, along with Harvard’s Prithwiraj Choudhury, explore the potential biases that limit the effectiveness of ML process technologies and the scope for human capital to be complementary in reducing such biases. Prior research in so-called “adversarial” ML looked closely at attempts to “trick” ML technologies, and generally concluded that it’s extremely challenging to prepare the ML technology to account for every possible input and manipulation. In other words, ML is trickable.

What should firms do about it? Can they limit ML prediction bias? And, is there a role for humans to work with ML to do so?

Starr, Agarwal and Choudhury honed their focus on patent examination, a context rife with potential trickery.

“Patent examiners face a time-consuming challenge of accurately determining the novelty and nonobviousness of a patent application by sifting through ever-expanding amounts of ‘prior art,’” or inventions that have come before, the researchers explain. It’s challenging work.

Compounding the challenge: patent applicants are permitted by law to create hyphenated words and assign new meaning to existing words to describe their inventions. It’s an opportunity, the researchers explain, for applicants to strategically write their applications in a strategic, ML-targeting way.

The U.S. Patent and Trademark Office is generally wise to this. It has invited in ML technology that “reads” the text of applications, with the goal of spotting the most relevant prior art quicker and leading to more accurate decisions.. “Although it is theoretically feasible for ML algorithms to continually learn and correct for ways that patent applicants attempt to manipulate the algorithm, the potential for patent applicants to dynamically update their writing strategies makes it practically impossible to adversarially train an ML algorithm to correct for this behavior,” the researchers write.

In its study, the team conducted observational and experimental research. They found that patent language changes over time, making it highly challenging for any ML tool to operate perfectly on its own. The ML benefitted strongly, they found, from human collaboration.

People with skills and knowledge accumulated through prior learning within a domain complement ML in mitigating bias stemming from applicant manipulation, the researchers found, because domain experts bring relevant outside information to correct for strategically altered inputs. And individuals with vintage-specific skills – skills and knowledge accumulated through prior familiarity of tasks with the technology – are better able to handle the complexities in ML technology interfaces.

They caution that although the provision of expert advice and vintage-specific human capital increases initial productivity, it remains unclear whether constant exposure and learning-by-doing by workers would cause the relative differences between the groups to grow or shrink over time. They encourage further research into the evolution in the productivity of all ML technologies, and their contingencies.

The post Machine Learning Has a Huge Flaw: It’s Gullible appeared first on Artificial Intelligence.

The size of Big data is really surprising, and it has just interwoven itself in core aspects of individual and business life. Customers are getting progressively mindful of their data privacy rights and data habits, while organizations have utilized such Intel to incredible impact.

A predominant topic today and going ahead, big data is ready to play a compelling job in the future. Information will characterize current medicinal services, government, finance, business management, marketing, energy and manufacturing. That implies gifted talent will be required over these businesses to address the difficulties of data analytics and help enhance improvements in products, services and society.

The application of analytics and the utilization of machine learning tools to derive value in information, otherwise called data science, is developing and we have quite recently started to expose what’s underneath. The three interlaced patterns of growing amounts of data, improved machine learning algorithms and better computing assets are forming the data science field in exciting manners.

Data Science is only a quantitative way to deal with an issue. Before, because of the absence of information or potential processing power, we depended on different things, similar to ‘authoritarian whim’, ‘expert intuition’ and ‘general consensus’. Today that doesn’t work by any means, and, question less, it will be even less compelling a long time from now. Data scientists, thus, are building systems that can talk, foresee, envision and give real outcomes.

The bubble around science abilities isn’t set to blast. Unexpectedly, the introduction of data-driven strategies will keep on picking up predominance. More individuals will see information, gain bits of knowledge from it, thus it might lead to the utilization of the data science team as an indispensable part of any fruitful company or, at any rate, most of them. It might even increase the rage of competitiveness and the want to be on the top.

It’s important to concentrate on the researcher aspect of a data scientist, accepting that a data scientist must have the option to detail a question or theory from observed information, sanction and test upon this theory and along these lines arrive at a conclusion and offer their outcomes. Noticing that most data scientists are just expected to produce repeatable models, challenging your data scientists to improve and enhance is genuinely where achievement lies. The absence of pushing data scientists to enhance their profession’s past essentially model deployment is a motivation behind why a lot of companies have difficulties with data science and AI.

We see colossal impacts of data science across businesses, however, some are more developed than others, especially in finance. They’re not really in the places you’d anticipate. We see enormous improvement being made and this is to a great extent is in light of the fact that these organizations have a ton of data as of now. Like finance has a long history of making data helpful, thus there is now a culture of being reasonably data-driven set up in a large number of these organizations, and they’re additionally keen on stretching out those capabilities to new sorts of information. As that is where we’ve seen individuals beginning to make unstructured data valuable in the manners that only structured data has been helpful previously, like text

Data science is working pretty intensely in the media also. That is things like understanding your crowd, helping them discover content they’ll cherish, helping them draw in with that content, ensuring it’s shared ideally across various platforms. It’s one spot, however extremely truly distributed.

The exponential growth in data we have seen since the start of our digital period will back off at any point soon. Truth be told, we have most likely just observed a hint of something larger. The coming years will realize a consistently expanding downpour of information. The new information will work as rocket fuel for our data science models, offering rise to better models as well as new and imaginative use cases.

Artificial Intelligence algorithms and within the sub field of deep learning, have progressed quickly over the most recent years. What’s more, there is an exceptional improvement in machine learning software. This is improving the quality of the algorithms and making the tools simpler to utilize, bringing down the barriers to entry for budding data scientists. In view of the solid reliance on machine learning tools, headway’s in this field straightforwardly impact the value and capabilities of data science.

The acknowledgment of these points of interest has driven the adoption of other AI applications, for example, machine learning and deep learning, the true future of data science. It goes beyond the limits of fundamental automation to deliver more prominent knowledge. Better and easier-to-use algorithms will emphatically impact data science by improving our present models and will empower the utilization of machine learning models for tasks that were recently saved for people. The organizations that can utilize and apply these algorithms in their business procedures will presumably build up a strong comparative advantage over their competitors.

The post Data Science is Transforming Businesses Worldwide appeared first on Artificial Intelligence.

Over the last few years, data has become one of the most valuable commodities for any business, driving decisions, powering new models and producing insights that can increase a company’s revenue or save it millions of pounds. Yet data alone can’t deliver real-world value – it’s only when it’s applied to projects and systems that these insights emerge. It’s data scientists that put in the research, thinking, planning and hard graft that transform data into tangible results.

To do so, data scientists harness their technical and analytical expertise, planning and developing data-driven projects that meet existing or future business needs. They help define any data architectures, models and operations research techniques involved in making those projects work effectively, and train their machine learning systems to spot patterns, forecast probable scenarios and predict results. They also work closely with teams across the organisation to spot new opportunities for data projects and ensure that the resulting insights become integrated into processes and workflows.

A data-driven business

At Tesco, data-driven projects impact every area of the business, helping it serve customers better. These projects influence everything from how much of a product to stock in a specific store to how online grocery orders are packed onto different vans. Data scientists will be involved in automated processes that recommend products to an online customer, based on sales data and their website interactions, but also in the way products are placed around the floorspace of a Tesco store. “We’re kind of a centralised team that works across different bits of the business,” says Stuart Barrow, head of data science at Tesco, “which means we try to improve how Tesco operates in different ways across different aspects.”

To do so, Barrow’s team works with a variety of rich, anonymised data sources, including sales data, data on purchasing behaviour, data from Tesco’s Clubcard scheme and data relating to the products Tesco sells. The data sources change according to the area of the business and the challenge, so that an online project might focus more on customer interactions with the websites and previous purchase history, while a project aimed at optimising supply chains will focus on historical sales data and how products flow in and out of Tesco stores.

Data scientists aren’t usually responsible for filtering and preparing data, or for coding, integrating and deploying the platforms that do the work, though they may be involved and work alongside development teams. Instead, their core responsibilities focus more on the data and on how it’s processed through analytics and machine learning.

“Often our work comes down to trying to predict or forecast something,” Barrow explains, “which means we will use that data in order to train machine learning models. A simple example might be forecasting sales in a store over the next couple of weeks. We’ll use historic sales data and other sources of information that we have in order to forecast the amount of products that will be sold, and that can then be used to inform how many products we bring into the store, and all the things that are interlinked with that on the supply chain side.”

Yet the impact of the data science team percolates into so many other areas. For instance, when a customer starts an order, a predictive tool goes to work to figure out how much the order might weigh and a scheduling optimisation tool decides which van it should go out on, even if the order might not be finished on that day. Meanwhile, the colleagues who picks the products for that order will be following a data-driven route around the store, defined by modelling its layout, the products stocked and the shopping behaviour of online customers.

Here small changes build up to have a real impact. When assembling an order, about 50% of a store staff member’s time is spent moving between each of the customer’s chosen items, so providing the optimal route saves them time and helps them not only ensure that the online customer gets the right products but also frees them up to help customers shopping in the store. Similarly, optimising the route of delivery vehicles saves time and fuel costs, while – at scale – helping reduce Tesco’s environmental impact.

Requirements for the role

Becoming a data scientist requires a high level of technical and mathematical expertise. At Tesco, the team looks for a good background in mathematics, physics or related subjects, plus a specialism in an area like machine learning or operational research. Experience of coding is a must-have, along with experience in open-source big data technologies such as Apache Hadoop or Spark. Beyond this, though, data scientists need flexibility and the ability to collaborate. “We can never deliver anything of real value on our own,” says Barrow, “it’s always as part of a bigger project, so we’ll often work closely within that team.” Project managers, data analysts and wider stakeholders within the business, including those within marketing and retail teams, will all be involved, and data scientists need to understand their specific wants and needs.

In turn, this enables data scientists to see new opportunities, or how solving one problem might be the first step in transforming something wider. “When working with my colleagues, it’s also occasionally an education piece when you do spot an opportunity they haven’t seen,” adds Ben White, a data scientist for Tesco. “Because we’re the data scientists, we know how the models operate and where they’re going to work well, and for which problems.”

Data science is a fast-moving field, which can make keeping up a challenge. “We need to make sure that we’re not missing opportunities to provide further value by making sure that we’re up to date with new techniques,” says White. Yet it’s this that keeps data science exciting, particularly in a company where you can work across so many areas and explore specific problems in real depth. What’s more, Tesco encourages its data scientists to spend Friday afternoons researching and training, to keep the team on the cutting edge. With a team of data scientists from different disciplines and backgrounds, there’s always opportunity to learn, upskill and share ideas. This can all be done remotely and around flexible working.

Working as a data scientist for a company of Tesco’s scale can be daunting. “It’s sometimes challenging to align the different parts and it’s a very big business,” Stuart Barrow notes. Yet it’s also hugely rewarding. “The most satisfying thing is when you get something out of the door and deliver value,” he says, particularly when you can measure the performance and see the tangible benefit to millions of customers. “When it works, the benefits are massive.”

The post How data science is transforming business appeared first on Artificial Intelligence.

Businesses need to understand and overcome the challenges in robotics so that the robotic solutions can be implemented in a manner that can provide maximum output and ROI.

The field of robotics is transforming multiple sectors at a rapid pace. However, it is still not advancing at the pace experts imagined it to. This slow growth in the field of robotics can be attributed to the challenges the industry faces. But what exactly are the challenges in robotics that are hampering the growth and advancement of the sector? Let’s look at the seven leading challenges that are affecting the robotics industry.

The field of robotics is facing hurdles with numerous hardware and software capabilities. Here’s a list of seven major challenges that need to be understood so that solutions can be developed to facilitate the adoption of robots on a larger scale:

Manufacturing Procedures

Manufacturing procedures in robotics haven’t seen many changes since ages. When it comes to hardware, a large number of industrial and commercial robots are still made from gears, motors, and actuators. These parts help robots in movement as well as for other functions, but make the robot rigid and unstable. Although The robots developed using the age-old manufacturing processes are highly prone to breaking down due to a large number of rigid, moving parts. This challenge can be overcome by developing robots that are flexible, less susceptible to damage, and have fewer joints and connecting parts. The field of soft robotics is an emerging area that can help design robots that are highly flexible and thus help businesses in carrying out tasks currently not possible with rigid robots.

Facilitating Human-Robot Collaboration

Robots can be better utilized when they are designed to work in partnership with humans. In an industrial setting, robots have to interact with humans in some form or another. However, human interaction is a major challenge in the field of robotics. Cobots are currently being developed that can work cohesively with humans. However, to make the ‘perfect’ cobot is a challenge. An ideal cobot must be able to understand human emotions, language, and behavior. They must respond in accordance with human emotional states and verbal communication. Creating fully-functional cobots that can exactly understand their human coworkers would require further advancement in NLU, NLP, NLG, and behavior recognition technologies. Only then will true human-robot partnership become possible and bring increased efficiency and productivity to organizations.

Creating Better Power Sources

Most present-day robots are highly inefficient in terms of energy consumption. Not much advancement has occurred in the development of power sources for robots. These robots still rely on age-old power generation and storage techniques. The batteries used in robots are usually unsafe and deplete quickly. The age-old power sources such as lithium-ion and nickel-metal hydride are still being used for robots. Thus, there is a need to develop new energy sources that can power robots for longer periods of time and also have high safety standards. Researchers are currently looking for methods that can replace the age-old batteries in robots, and a breakthrough can power up the robotics sector.

Mapping Environments

Robots are still not able to navigate through obstacles in their path. Even if a robot is trained to understand its environment, the slightest of alterations require the robots to re-learn and adapt to new environments. This can possibly lead to delays in carrying out the assigned tasks or even cause accidents. Machine learning and computer vision technologies are currently being leveraged to overcome the mapping challenge. However, these technologies aren’t foolproof and function best only under controlled environments. Additionally, real-life scenarios are highly unpredictable. No matter how trained the robot is or how good its adaptability to new environments, there always arises a situation for which the robot is not prepared for. For example, autonomous cars perform best under controlled conditions but have proven to be unreliable in real-world cases. There have been instances of accidents caused by autonomous vehicles, injuring and killing humans, even though they were trained to avoid such mishaps.

Minimizing Privacy and Security Risks

With any technology, there is always the question of privacy, ethics, and security. The data used for training the robots can be misused by reprogramming or modifying it, causing the robot to malfunction. Similarly, the data that the robot collects in its life cycle, too, such as videos, images, and location details can be hacked into and used for malicious purposes by fraudsters. Thus, ensuring the safety of the data always remains a major concern when using robotic solutions. There is also the question of how much we can be dependent on robots to carry our daily tasks. If we need a robotic solution that can completely assist us or replace us in carrying out tasks, then it needs to be given sensitive data by users willingly, which again is susceptible to hacking.

Additionally, there is no clarity on the ownership of the data the robot has. There remains a dispute whether the owner of the data is the end-user, the robot manufacturer, or its software provider. There is also no clear guideline as to which party can use what part of data for what purposes. This raises the question of whether the data will be used ethically by the party that has ownership over it.

Developing Reliable Artificial Intelligence

Robots are usually programmed using artificial intelligence and machine learning technologies. However, despite advancements in these technologies, we still haven’t reached a stage where the technologies can be trusted completely. Firstly, tons of data is required to train robots to carry out their designated tasks. Even then, it is not guaranteed that the robots will work as intended, as the robots are usually trained under controlled environments. Real-world environments can sometimes become challenging for robots to comprehend and take suitable action. Currently, artificial intelligence is no match for human reasoning, and, thus, robotic solutions aren’t foolproof or completely dependable.

Building Multi-Functional Robots

Robots can carry out a single task efficiently. However, a single robot cannot perform multiple functions with the same efficiency or efficacy. Robots that are deployed in industrial settings are usually static and carry out a single repetitive task. However, with increased competition, businesses need to deploy robots that can multi-task to cut costs and help improve productivity. Robots need to identify people, objects, and environments while simultaneously interacting with them in an industrial setup. For example, a robot deployed at a warehouse to place objects needs to pick the object from a human employee, navigate his way to another section of the warehouse, and place the object in the rightful place. However, the robot may not be able to work cohesively with multiple elements and may not be able to carry out all the tasks. Hence, robots need to be developed with advanced versions of artificial intelligence, NLU, and machine learning technologies and superior hardware so that they can carry out multiple tasks with ease.

Agreed, the robotics industry has its own set of challenges. It has a long way to go before we can truly enjoy the benefits the industry can offer. However, businesses shouldn’t be disheartened and skeptical of using robots at their workplaces. The challenges in robotics are slowly but surely diminishing with advancements in technology. The current robotics solutions, too, prove highly beneficial for businesses in manufacturing, logistics, transportation, and healthcare. Businesses just need to find the most suitable solution that they can employ at their workplace by collaborating with an experienced robotics solutions provider. They can rest assured that the challenges mentioned above won’t interfere and hamper the productivity of the business if the robotics solutions are implemented correctly.

The post THE 7 BIGGEST CHALLENGES IN ROBOTICS appeared first on Artificial Intelligence.

At first, you might find the idea of Artificial Intelligence/Machine Learning being associated with Law very unlikely since both the fields appear to be poles apart. Well, the truth is far from it; today, Artificial Intelligence or AI is on the way to transforming the legal profession in various ways, helping Law firms manage their operations as well as augmenting and reducing many of the tasks that were previously relied upon humans to do, saving precious time and manpower that can be otherwise used for more productive tasks.

Artificial intelligence, on the base level, aims to develop and find ways to reduce, manage and execute laborious tasks for different industries, automating many of the operations that would otherwise require human input. These solutions are found to greatly improve the speed as well as drastically reduce the errors, hence improving the accuracy.

Whenever a new technology is introduced to the World, every sector and industry are offered the possibility to adopt that to enhance their operations. One example is of computers and how they quickly grew in use, taking over many of the manual paperwork, and how they have become essential today in almost every office and profession. Law firms are no exception to that, where technology has always been forefront and finds its way into supporting the lawyers, paralegals, researchers and clients alike which are associated with the profession.

Through this article, we will be looking at and discussing the different ways in which AI and machine learning are applied specifically in the legal profession for the streamlining of their operations and work processes. Let us start by briefly understanding what artificial intelligence and machine learning truly are.

What is AI and Machine Learning?

The term Artificial Intelligence is meant to describe intelligence exhibited by machines that try to copy or mimic the human cognitive in certain ways, such as problem-solving and learning. This concept is widely being accepted and utilized to substitute those tasks that require human intelligence.

Machine learning denotes the phenomenon where computers are fed with data sets and are made to analyze different patterns from that data, learn from there and then use that learning to gain useful insights. Different algorithms are involved which aim to let the computer carry on the task without the need for human intervention.

The Impact of AI on the Legal Profession?

There is no doubt that AI has already started impacting the legal sphere in ways you might have noticed as well (if you belong to the profession, that is). Research has shown that a lot of manual work required in Law firms have been substituted by artificially intelligent machines. Since the operations are greatly improved and streamlined, as per different studies, Law firms have no choice but to embrace this new wave of technology and accept AI and machine learning in their daily operations. Below, we describe some of the different applications that Law firms have found with AI.

Documents review 

A great part of any legal proceeding is the amount of documentation that needs to be skimmed through to find the relevant material, which requires a team of paralegals and hours and hours of precious time. Firms have adopted AI software that helps to analyze documents and flag the ones that are deemed as relevant.

Legal Research 

Once the relevant documents are shortlisted and flagged, machine learning comes into work and uses the learned algorithm to find similar documents that can be of use, out of the millions of papers, proceedings, and dissents. This ensures that all the legal research is done very efficiently and in a comprehensive way, covering a whole lot more data. Natural language processing or NLP is also applied to further analyze the documents to aid the research.

Due Diligence 

This is hectic work, where legal professionals are required to perform exhaustive background checks and go to lengths to uncover information regarding their clients or on their behalf. These facts and data are then evaluated for better decision making and are used to support their cases and giving sound counsel to the clients. This tedious work is fast being replaced by artificially intelligent systems, which help perform most of the due diligence more efficiently and accurately.

Contract Management 

Often, clients come for legal counseling to review contracts and to check and identify if any issue or risk is associated with that contract. Sometimes, contracts can be misleading and have a negative impact, and legal professionals assist their clients to avoid just that. AI can be used and is being used, to analyze such contracts in bulk and made to learn to identify such situations quickly with fewer human errors, avoiding any mishap. 

Predicting Outcomes

Since computers and artificially intelligent systems have access to huge amounts of trial data and years of documentation, pattern recognition, and machine learning comes into play in the analysis of all that. These insights are then used to predict outcomes for similar cases as well as finding answers that could have otherwise taken days to arrive.

Conclusion

According to recent research, many and more of the legal roles will be automated and be replaced by artificially intelligent systems. The time is ripe, therefore, for Law firms to accept and embrace this new technology change or watch as others who do fly past.

Already, Artificial Intelligence and machine learning, hand in hand, are transforming the legal sector where organizations have come to realize that technology and innovation is the key to success. Already, these intelligent systems have showcased their superiority over humans, statistically speaking; numerous reports and studies will tell you just that. Especially for Contract management and legal research have shown a lot of promise.

Even though some would argue that manually reviewing documents can prove to be accurate and humans can compete with the threshold that is left by the machines, there is no doubt about the fact that AI systems are proven to be no match when it comes to speed. They get the work done in hours which would have otherwise taken a team of paralegals to perform in days!

The post HOW AI AND MACHINE LEARNING ARE TRANSFORMING LAW FIRMS appeared first on Artificial Intelligence.