European Commission president Ursula von der Leyen has unveiled a set of proposals designed to level the playing field in AI between European companies and their competitors in the US and China. The initiative also aims to address the challenges digital technologies pose in areas such as data privacy and algorithmic bias, and create confidence and trust among citizens so the benefits promised by these technologies can be fully exploited.
With its latest digital strategy, the commission aims to encourage more cooperation between public and private sectors, with a view to creating a digital single market across the continent, a goal it has been pursuing for years with only limited results. Some experts voiced their disappointment, however, calling for a more transparency regarding the new policy.
Dataiku, one of the world’s most advanced Enterprise AI platforms, has been named a Leader in the Gartner 2020 Magic Quadrant for Data Science and Machine-Learning Platforms. Dataiku’s platform enables data democratisation in businesses by putting data directly into the hand of business users.
More than 300 customers, primarily across retail, e-commerce, healthcare, finance, transportation, the public sector, manufacturing and pharmaceuticals, use Dataiku to scale their AI efforts.
Soft robots – which are made from pliant materials and typically used in medical and manufacturing applications – by definition need to be flexible, so are frequently made from soft materials such as paper, plastic or rubber. In December 2019, a team of researchers announced that, by combining metals such as platinum with ash derived from burned paper, they had developed a new material with enhanced capabilities as well as the foldability and lightweight features of paper and plastic. These characteristics make the new material a strong candidate for making prosthetic limbs up to 60 per cent lighter than their conventional counterparts.
The conductive material also has geothermal heating capabilities when charged with electricity. The discovery also gives a major boost to the development of origami robots – flat sheets of metal or plastic that fold out into bots that can walk, climb or swim and can be deployed in drug delivery inside human bodies.
Another emerging use case of origami robotics is a foldable device that can fit in a pocket and transmit touch stimuli when used along with VR interfaces, enabling e-commerce customers to feel virtual examples of textiles and materials of products they are considering buying.
Apple co-founder Steve Wozniak and tech entrepreneur David Heinemeier Hansson have lodged complaints about the gender bias of the Apple credit card, the first Goldman Sachs product of its kind. Both claimed that they were assigned a credit limit many times higher than their wives, despite the latter having the same or even better credit scores. In response to the complaints, New York’s Department of Financial Services (DFS) began an investigation declaring that “any algorithm that intentionally or not results in discriminatory treatment of women or any other protected class violates New York law.”
Information technology consultant Booz Allen has launched an AI platform and marketplace, Modzy, aimed to make it easier for businesses to discover, deploy, manage and govern AI models securely. The Modzy platform and model marketplace was designed to reduce the risks and barriers to AI adoption by creating the missing AI layer in today’s tech stack, and provide access to a growing list of AI models from leading tech companies and open source communities. Click here for more details.
Swiss automation company ANYbotics AG has announced that it has upgraded its canine robot, ANYmal, for industrial inspection, which now can provide high availability, safety, and reliability for automated routine inspections. Autonomous mobile robots are expected to revolutionise industrial inspection thanks to their ability to execute pre-defined missions and navigate industrial plants to collect and interpret equipment and environment data via sensors.
by Zita Goldman, Business Reporter
Buzzwords and jargon explained
AI refers to a machine’s ability to replicate the cognitive functions of a human being. Today there are three recognised levels in the AI spectrum: assisted intelligence for the execution of basic tasks such as those performed by machines on assembly lines; augmented intelligence, where AI learns from human input; and autonomous intelligence, with humans out of the loop.
Autonomous mobile robots (AMRs) are capable of navigating an uncontrolled environment without the need for physical or electro-mechanical guidance devices.
An artificial neural network (ANN) is is an attempt to simulate the network of neurons that make up a human brain so that the computer will be able to learn things and make decisions in a humanlike manner. Typically, an artificial neural network has anywhere from dozens to millions of artificial neurons – called units – arranged in a series of hierarchical layers. The majority of neural networks are fully connected from one layer to another though a weighted system of connections which behave in a similar manner to the human brain.
Bias in supervised learning can occur when a model that is unable to capture the underlying pattern of the data results in an algorithm latching onto a feature that is meaningless. The algorithm is performing accurately, but is actually doing it for the wrong reasons – for example, recognising fractures on X-rays because they come from hospitals with a consistently high number of fractures.
A data scientist is someone who predicts the future based on past patterns discovered in data, whereas a data analyst is someone who curates meaningful insights from data with the help of predictive models and machine learning capabilities programmed by data scientists. The difference between the two jobs can be less clear-cut in real life situations, however.
Data wrangling is the process of transforming and mapping data from one “raw” data form into another format with a view to making it more appropriate for purposes such as data analytics.
Deep learning is a type of machine learning that uses artificial neural networks (ANNs) in order to facilitate learning at multiple layers rather than task-based algorithms. Yoshua Bengio of Canada’s MILA institute, Geoffrey Hinton of the University of Toronto and Yann LeCun of Facebook, the man behind Facebook’s facial recognition and photo-tagging software, are as a group often dubbed as the godfathers of deep learning
Hyperautomation is a combination of tools marrying robotic process automation (RPA) with tools such as intelligent business management software (iBPMS), AI, ML, and analytics with a goal of increasing automation capability. Hyperautomation is pushing RPA from the back-office into front-office functions such as sales, customer experience, and service.
ImageNet is a large visual database designed for use in visual object recognition software research. Within the project 14 million images have been hand-annotated to indicate what objects are pictured, and grouped into 20,000 categories – with a typical category, such as “table”, consisting of several hundred images. ImageNet was the dataset that started the current machine-learning boom.
Natural Language Processing, or NLP, is a field of AI that gives the machines the ability to read, understand and derive meaning from human languages by understanding the context of words within paragraphs, and not simply the meaning of individual words. NLP should also take account of slang and regional variations in meaning.
Origami robots are autonomous machines whose morphology and function are created by folding. Their bodies are made of many dynamic folds that act together to operate the machine. For more about origami robots, check out this TED Talk by roboticist Jamie Paik.
Process mining is a family of techniques in the field of process management that support the analysis of business processes based on event logs.
Robotics is the science and engineering of devices which are reprogrammable, multi-functional, multi-purpose and versatile systems intelligently linking sensing to action, qualities. This makes it distinct from automated systems, which are special-purpose machines or systems designed to perform a specific task.
Robotic process automation, or RPA, is a term used for software with AI and ML capabilities that partially or fully automates human activities that are manual, rule-based, and repetitive. RPA bots – including basic ones, as well as chatbots and other types of virtual agents – can log into applications, and can then manipulate data, trigger responses, initiate new actions and communicate with other systems autonomously. What distinguishes RPA from traditional IT automation is its ability to be aware and adapt to changing circumstances, exceptions and new situations.
Soft robotics is the science of robots that are soft, flexible and compliant, and have similar properties to their biological equivalents.
Synthetic data is any production data applicable to a given situation that are not obtained by direct measurement.
A transformer is a data manipulation language (DML) model introduced by Google in 2017, which has enabled training on much more data than was possible before it was introduced. It is now the basic building block of most state-of-the-art architectures in NLP.
Source: Zita Goldman, Business Reporter
Ian Firth, VP Products, Speechmatics
Speech recognition is in great shape – accuracy levels are good and improving all the time. The accuracy is no longer focused on the easy scenarios, but is now being used for noisier, harder conversational use-cases, making the technology practical for real-world applications. This is supported by the ability to deploy the technology in scalable ways that meet business needs, offering on-premises models as well as a public cloud.
What’s ready today?
The way it is consumed is getting easier too. Speech recognition can support things like multi-accents and dialect models to avoid the challenges of managing deployments for the diverse world that we live and operate within. Speech technology is not just for English either – it also supports native speakers of a growing range of many different languages. The capabilities of speech technology are ever increasing, enabling businesses to operate globally with the same scale and support that they would have in the English-speaking world.
Current challenges the industry still faces
There is always greater possibility in any industry. Non-English support for speech recognition is not as good as it is for English in many cases, especially taking into account accents and dialects. With the support for multiple languages comes the challenge of understanding which language is being spoken. This means that the ability to detect and decipher language itself is still a growing need. Language identification and detection and code switching are now becoming increasingly important to the adoption of speech technology, but still remain a challenge for most speech technology providers. Personalisation to specific users and use-cases is still very much a challenge but the foundations have been laid with features such as custom dictionaries and are expected to get better in the short term.
It’s not just words that are used to convey meaning in conversation. Sentiment, the speaker, hesitation and non-speech sounds all provide additional context and meaning. There is still work to do here to enable the wider meaning of speech to be determined.
What’s the potential for speech technology over the next few years?
Ultimately, what we really want is to truly understand the spoken word, not just transcribe what is said. That is the journey that the technology is now very much on. Understanding means supporting continuous intelligence within businesses. Enabling that understanding in real-time enables actions to be undertaken in line rather than out of band. Understanding also means using all the available context. So, that means looking wider than just the words. It means listening to sounds and sentiment but it also means using images, video and textual forms of communication that might be available to provide the deeper meaning of the communication. As speech technology continues to develop, we expect to see a broader range of useable outputs from speech analysis such as call-steering, detailed sentiment and extending voice control capabilities.
All of this advancement needs more and more data to be processed. The long pole here is having enough labelled data to support the learning required. We are undertaking some research to enable this to be less human-intensive and provide much faster learning that is continuous. These developments will unlock the power in understanding that will form the next big step in speech recognition technology.
Find out more about benchmarking speech technology providers here.
At a recent gathering of customer service leaders discussing future digital and AI strategies, we laughed at the tale of a customer’s order for a 7th birthday candle being substituted with a one and a six candle by the robotic picker. Ingenious, yes – but what seven-year-old would want a 16 or a 61 on their birthday cake?
It’s a prime example of automation being effective in theory but failing in practice. In the years ahead there will be an explosion in learning exchange between robots and humans, but the skills needed to ensure we progress to higher standards of service in this evolution (revolution?) requires skills that we perhaps don’t have enough of yet.
Since the dawn of the smartphone, there has been a huge learning curve for us all on technology applications. Technological capabilities are most definitely there to take the heavy lifting. Where most are struggling is best and appropriate application. Companies, and most importantly suppliers of tech, need to be extremely clear on the business problem that needs solving versus a ‘chasing competitors’ mentality.
CCA research in 2019 examined some of the biggest challenges faced by customer experience professionals. Some specific challenges that were identified related to technological investments, suggesting organisations are moving towards making those difficult decisions around where to focus their attention.
The ‘why’ has never been more important, given that public trust in government and business is now at an all-time low. Building trust is critical, as is a constant focus on eliminating ‘friction’ that can cause parts of the system to stop working.
Companies are trying to coerce or nudge customers to switch to cheaper channels of communication and do more for themselves. Depending on how this is sold to us, it can have very different outcomes. If there’s a perceived benefit and a smooth, easy-to-use service, we’ll adapt well; if it’s a clear cost-focused reduction in service, it will likely cause problems.
To change behaviour, we must show customers what’s in it for them. After a few years of trying to get customers to use other channels instead of calling, many brands are faced with increased costs and no reduction in voice contact. Much of this has to do with a failure to identify customer-led efficiencies, or a reason why it is appealing for a customer to change behaviour. There are examples of success beginning to come to the fore, and equally a realisation by others that they are unlikely ever to meet the earlier transition targets as they have been based on unrealistic expectations of how customers actually behave.
There is an overwhelming desire for all organisations to prove the often-burdensome collection of customer data is used genuinely for the benefit of customers and not simply amassed to sell more to them. The rubbing in today’s interactions across all sectors may not be tolerated in future, as new concierge and group purchasing models will spotlight and challenge organisations that continue to build processes around themselves rather than customers. In particular, open banking and shared data in public services are major driving forces towards this shift.
To meet complex consumer needs in a time of uncertainty and change, the human touch in customer contact is critical. While it might be possible to automate routine interactions, sensitive and difficult situations require empathy and understanding – fundamentally human characteristics. As AI and robotics are increasingly used, roles will become more focused around customers who wish to talk to humans rather than voice systems. It must feel individual as the demand for deeper personalisation and richer context around every conversation grows.
It’s a tall order for our network, but many are rising to the challenge.
Anne Marie Forsyth is the CEO of CCA Global Ltd. Progress through to 2021 will be led by CCA visionaries – a group of 40 leading blue-chip and public sector brands committed to developing and delivering new thinking. For more information on how you can participate, please get in touch.
While it has not yet arrived, the time is approaching in packaging and processing manufacturing when it will be nearly impossible to discuss advancements in automation without addressing congruent developments in artificial intelligence (AI). AI and automation are particularly intertwined in robotics, supplying workforce-enhancing solutions that not only complete repetitive tasks but also support and improve decision-making processes.
Already, industrial robots with a high risk of breakdown can self-minimise downtime by pre-scheduling maintenance and ordering spare parts or by analysing vision system and sensor data to reduce the time taken to complete a task. Network-connected robots use AI to either learn simultaneously or from one another, reducing the time it takes to understand new jobs. AI with machine vision systems is also supporting significant developments in quality control, enabling robots to identify faulty products and remove them from the production process without human intervention.
AI-enhanced automated packaging plays an extensive role in ensuring the supply chain can handle the increased workload created by e-commerce. At a macro level, automation that can think for itself provides integrated solutions to compensate for lack of labour. More targeted solutions, however, are proving that automation can increase a packaging operations’ bottom line while improving the efficiency of the supply chain.
Smart robots equipped with advanced sensors that feed data to complex algorithms powering AI and machine learning will further improve work processes and the supply chain, so much so that collaborative robots (cobots) might represent the model application for AI and automation. Unlike robots that are traditionally isolated from workers and are programmed to follow specific instructions without regard for humans, cobots operate in cooperation with humans in a shared workspace. In fact, cobots represent the fastest-growing segment of industrial automation. They are expected to jump tenfold to 34 per cent of all industrial robot sales by 2025, according to the International Federation of Robotics. This statistic goes hand-in-hand with advances in AI capabilities that will pave the way for further growth in autonomous robots. Adaptive robots that are capable of learning can recognise inefficiencies and make changes on-the-fly to operate more effectively.
AI is also being used to advance end-of-arm-tooling (EOAT) changeovers. Production runs in the packaging and processing industries continue to get shorter, making downtime costlier than ever before. There is now the capability for a robot to understand what’s coming down the line, change its own EOAT to meet the immediate need and then perform its task.
Mobile robots and cobots will grow in use as AI continues to reach higher levels of intelligence. The advantages are vast as untethered or wireless robots with seventh axis movements will create a flexible manufacturing environment.
Robots are also going to be safer to operate around humans. The machines can learn tasks efficiently using AI, not only to improve processes, but to avoid collisions and reduce risk. This model could permit robots almost anywhere on the manufacturing floor. For example, if a worker walks too close to a working robot, the machine will go into “safe mode”: operating at a slower speed, with limited force and more controlled actions. When the worker leaves the area, the robot will resume full performance.
While it can be challenging to manage the balance between replacing humans with automation and increasing unemployment, companies can address this by supporting employees through retraining programs to enable workers to “upskill”. One leading robot manufacturer recently explained how, in its workforce, it had replaced workers with robots for metal casting, which was a dangerous job. The company worked on retraining employees for newly created roles, such as application engineers and designers – jobs that didn’t exist within the organisation eight years ago. This model of retraining the workforce can also be a cost-effective method of acquiring new skillsets within the organisation, with retraining often a lower-cost option than recruiting new employees.
The PACK EXPO portfolio of trade shows, produced by The Association for Packaging and Processing Technologies (PMMI), provides the latest innovations and technologies to encourage and assist with AI and automation. Seminars on the show floor at the Innovation Stage and The Forum also open educational gateways for best practices and new applications, as well as interactive discussions on what has worked for others in the industry.
The next stop in the PACK EXPO portfolio of trade shows is PACK EXPO East 2020 (3-5 March; Pennsylvania Convention Center, Philadelphia). The three-day event will bring together 7,000 attendees, with 400 companies showcasing new technologies in 100,000 net square feet of exhibit space. PACK EXPO East attendees will enjoy all the educational and networking opportunities traditionally offered at PACK EXPO, plus more face-to-face time with exhibitors to find applicable answers.
Sean Riley is Senior Director, Media and Industry Communication for PMMI. For more information and to register for PACK EXPO East 2020, go to packexpoeast.com
Your guide to the latest research and whitepapers
Robotic process automation, or RPA, is on the cusp of maturing, suggests a white paper by Sdlc Partners. It quotes Gartner’s definition of hyperautomation, the top tech trend for 2020, as “an approach in which organisations rapidly identify and automate as many business processes as possible”.
Hyperautomation, as the white paper explains, has RPA at its core, complemented with so called co-RPA technologies such as process mining, analytics, machine learning, customer experience, iBPMS (intelligent Business Process Management System), and iPaaS (intelligent Platform as a Service). Hyperautomation is shifting RPA from the back-office into front-office functions such as sales, customer experience, and service.
This new approach to automation has attended (desktop), hybrid and unattended types as well. The report includes a roadmap of a robotic process automation journey listing milestones and necessary resources, as well as a step-by-step guide to upscaling.
McKinsey’s Global AI Survey offers a high-resolution snapshot of current AI uptake, based on the answers of respondents about 33 AI use-cases across eight business functions. Its charts offer an excellent overview of which functions (ranging from marketing and sales to manufacturing to risk management) tend to generate the highest revenue streams or result in the highest cost savings, as well as of the adoption rate of AI capabilities as diverse as physical robotics and virtual agents.
While more than half of respondents expected a 10 per cent rise in adoption, 30 per cent of AI high performers anticipate their organisations would increase investment in AI by 50 per cent or more in the next three years. The report also contains a comprehensive, easy-to-read exhibit illustrating which AI capabilities – such as RPA, computer vision, machine learning, natural language understanding, virtual agents and robotics – have the highest levels of AI deployment within specific industries, as well as the combined growth rates on 2018 figures.
Businesses do not, however, tend to be fully aware of the potential threats AI poses, nor of the ways they can be mitigated, with less than half of respondents saying they identified and prioritised AI risks. Responses suggest that the workforce-decreasing effect of AI hasn’t fully hit most industries yet. However, the automotive and telcomms sectors have seen the deepest cuts so far. To study the report go to
Some ingredients are fundamental to any AI and machine learning deployment. The first, which often tops the list of challenges that banks and other businesses face, is the cleaning and wrangling of data.
Along the whole AI pipeline, a delicate balance needs to be struck between centralisation and distribution. In the case of data, for example, it is ideally kept in a central place where it can be accessed by any users across the whole organisation, via a user-friendly interface. Although a centralised organisation structure with a central data management team can bring benefits too, in its decentralised counterpart employees are upskilled as data analytical experts to avoid the often cumbersome and time-consuming communications between employees with the subject knowledge and data specialists.
Retraining employees with a statistical, actuarial or quantative analysis background may turn out to be an easier way of filling in skill gaps than familiarising externally recruited data experts lured from fintechs with the workings of banking and finance. An important selection criterion for finding the right software vendors, another key step of the AI pipeline, is to check whether they offer any open-source solutions with extra layers that make them more user-friendly. Centralised data platform provider Daiku’s white paper offers plenty of AI use cases in banking and examples of best practice, as well as an account of how a former trader has become a successful data scientist.
Source: Zita Goldman, Business Reporter
The word “automation” can be interpreted in many different ways. Back in the 1970s, when computer-aided manufacturing was developing, the invention of computer-numerical control (CNC) was considered a revolution in terms of automation.
It’s easy to forget how many of our everyday processes derived from a manual method, yet 50 years later the marvels of automation are all around us. Even a clock is just an automated version of a sundial.
However, when the clock became the most common timepiece, this didn’t render sundial makers jobless. It meant that those who previously spent their time making sundials could now enhance their skill and learn how to make a clock while using the valuable experience they’d already gained through working within the time-telling industry. It is important to accentuate the opportunity that modern automation is providing us with.
Just because machines are growing smarter and more intelligent doesn’t take anything from human potential. It means the workforce of the future needs to be trained to control these smarter machines. Design and technology lessons only became part of the national curriculum once a requirement for jobs in the field had emerged. Suppose how to defend against cyber-attacks was part of the curriculum – or potentially, instead of learning an international language, there is an option to learn a machine language – wouldn’t it begin to shape our future workforce from a younger age? Our current workforce has developed the necessary skills through what they learn at an early age, and if future generations are taught how to coexist with automation from a young age, naturally more jobs will be created. Essentially this is modelling the growth potential of our future industry as opposed to cutting costs through replacing the workforce.
Automation is the technique of making an apparatus, a particular system or a process, operate without interference or automatically. It’s much more than simply using robots to do everything. Whenever I’ve mentioned the word automation, I get a very different response depending on who I’m speaking to. Or more accurately, on what automation means to them. Automation is often present in their lives, but there is an underlying fear that it may take over.
While automation may improve the efficiency of processes we are used to, there are many other areas in which human skill is irreplaceable and this must not be forgotten. Automation will of course replace somewhat tedious tasks but won’t necessarily replace complete job profiles. And while the debate stands as to whether great managers are made or born, I’m sure many would agree a robot can’t replace a good manager. A robot lacks empathy, social intelligence and the ability to be creative so it’s not possible to substitute one in jobs which require the use of these skills. This is merely one example where automation should assist, not replace.
Indeed, in industrial automation there are jobs which robots are able to do more repeatably than humans, and for longer – but there is also a whole skillset which robots are not able to reach. Robots are able to learn – however, they lack the ingenuity and experience that humans have gained in the past.
As we digress into a world where connectivity and digital devices are beginning to be commonplace, it is important to remember that it is because we are able to adapt to these variations of understanding of one word, that we are not going to “be replaced by a robot”. Humans have the unrivalled ability to innovate, and while artificial intelligence is enabling machines to “learn”, it doesn’t permit the ability to create using imagination – an unparalleled skill.
by Nikesh Mistry, Sector Head, Industrial Automation, GAMBICA
If you would like to share your opinions with like-minded people as to how the digitalised world is evolving then enquire with us at www.gambica.org.uk.
Maritime organisations are often reluctant to report cyber-attacks for fear of reputational damage. But now an anonymous reporting system, set up by UK-based maritime membership organisation the CSO Alliance to ensure anonymity and confidentiality, is making strides towards addressing this problem, according to this piece in shipping journal Lloyd’s List. The piece includes an overview of the International Maritime Organisation’s guidelines to maritime cyber-security risk management, which indicate the importance that shipping companies understand the nature and volume of these cyber-attacks and, in turn, raise their preparedness and do their part to mitigate the risk of subsequent events.
Three prominent women in AI talk about bias in machine learning in this interview – Daphne Koller, founder of Insitro, a company using ML to develop new drugs; Olga Russakovsky of ImageNet; and Timnit Gebru, a research scientist at Google’s ethical AI team.
Bias means at least two different things when it comes to machine learning. For a layperson, what comes to mind are the examples of racial, gender and age bias against people of colour, women and the elderly. However, for AI experts, a bias is primarily when, as Russakovsky puts it, “an algorithm is latching onto something that is meaningless and could potentially give you very poor results.”
In other words, the algorithm may come up with the right results but for the wrong reasons. For example, it can correctly identify a fracture on an X-ray only because it comes from a hospital that happens to have a high number of cases. Gebru, on the other hand, draws a link between the two types of bias by claiming that feeding a more diverse data set to algorithms is not the silver bullet – we also, she explains, need “to have principles and standards, and governing bodies, and people voting on things and algorithms being checked.”
With employee retention becoming increasingly hard, deploying AI tools to personalise and maximise the employee experience has become key to successful talent management. AI has the potential to free HR from mundane tasks, such as sorting through hundreds of CVs or scheduling meetings.
To start with, HR departments need to identify the processes that need the most improvement. Annette White-Klososky, writing for Forbes, maintains that even those on the non-technical side of a business need to have at least some basic understanding of AI to appreciate its full potential – for example, how it can reduce staff turnover through sentiment analysis, or help with coaching – to better pitch AI-enabled technologies to the C-suit.
According to IBM’s Institute for Business Value, the need for reskilling and retraining due to the impact of AI and automation technology will affect more than 120 million workers across the world’s 12 largest economies.
In the UK, only 41 per cent of employers have the required people, skills and resources in place to execute their business strategies effectively. In addition to digital skills and qualifications in STEM, there is a growing demand for soft skills such as learnability and cognitive flexibility, which the labour market can’t meet. The situation is further aggravated by the recent drop in the “half-life” of professional skills – which means that skills learned now will only be half as valuable in five years’ time – from 10-15 to five years. Meanwhile, the time needed to close skills gaps using traditional training approaches, such as classroom and virtual learning, has increased by a factor of 12 – from three days in 2014, to 36 days in 2018.
Source: Zita Goldman, Business Reporter
Automation and AI events, expos and get-togethers to add to your calendar
International Conference on Automation and Artificial Intelligence (ICAAI)
This event gathers hundreds of research-minded people from AI and its related fields for an opportunity to make new contacts in AI and automation engineering and a platform to share new ideas relating to recent technological developments. Click here for more details.
AI and Automation for Mobile Banking and E-Commerce
Bloomsbury Hotel, London
The second such forum hosted by digital education and skills provider QA will map the new software technology landscape of open banking and e-commerce and the new benchmarks for mobile app quality assurance. Click here for more details.
AI and Big Data Expo Europe
A showcase of next-generation technologies and strategies from the world of AI and big data, providing visitors with an opportunity to explore and discover their practical and successful implementation to drive businesses forward in 2020 and beyond. Click here for more details.
Robotic Process Automation Summit
The Robotic Process Automation Summit is aimed at businesses looking to streamline processes, increase productivity and ultimately improve the bottom-line. Click here for more details.
Source: Zita Goldman, Business Reporter