If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Martin Taylor is the Deputy CEO and Co-Founder of Content Guru. In the past couple of years, contact centers have suddenly undergone an extreme character makeover, from asset-sweating tech laggard to leading light in intelligent automation. How has this corporate ugly duckling turned itself into a digital swan? Under pressure to differentiate service offerings and add personalization, many organizations have been quietly deploying key AI technologies -- especially natural language processing (NLP), image recognition and data analysis. The contact center's application of these general-purpose AI technologies is transforming how they model and predict call volumes, enable new automated self-service channels and evolve the role of their oft-maligned workers.
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For the second part of this article series, see here. It has only been 8 years since the modern era of deep learning began at the 2012 ImageNet competition. Progress in the field since then has been breathtaking and relentless. If anything, this breakneck pace is only accelerating. Five years from now, the field of AI will look very different than it does today.
DWS's stake in Arabesque showed how asset management is moving towards AI-powered investing. Artificial intelligence, hailed as investing's next frontier, is already widespread in various forms, but its true potential in portfolio management is still far from being fulfilled. In a study on AI and finance for the Alan Turing Institute, Professor Bonnie Buchanan puts AI's "impressive" growth down to declining processing and data-storage costs, and an immense availability of data. But compared to other fields, the quantity of data or the ability to create and collect new investment data is still not sufficient, despite its abundance, according to Michael Neumann, head of AI quant investing at Arabesque AI in London. Financial data also comes with a lot of'noise', and the definition of success or failure can be more nuanced.
When you look more closely at estimates that one-third or one-half of jobs will be "automated," the evidence actually tends to show that one-third to one-half of jobs will be changed in the future by use of technology. Maybe some of those jobs will disappear, but in many other cases, the job itself will evolve, as jobs tends to do over time. Of course, it's a lot less exciting to have a headline which says: "The information technology you use at your job is going to keep changing change in ways that affect what you do at work." Qualifier 2 – job creation from automation An overall view of the effects of automation on jobs also needs to take into account how, over time and in the present, automation has also led to the creation of many new jobs. Lest we forget, the US unemployment rate before the pandemic hit was under 4%, which certainly doesn't look like evidence that total jobs are being reduced.
Amazon Web Services is rolling out a series of new tools within its industrial Internet of things lineup that aim to improve machine performance and uptime. First up the company announced Monitron, a condition monitoring service for customers that currently lack an existing sensor network. The system and its array of sensors can detect potential failures on critical equipment, allowing for the implementation of a predictive maintenance program. For those customers that do have an existing sensor network, AWS introduced an API-based machine learning (ML) service called Lookout for Equipment that functions as a pathway to send sensor data to AWS for predictive modeling. Like Monitron, Lookout for Equipment analyzes sensor data to detect abnormal behavior on industrial machines.
At the end of a three-hour keynote address for Amazon's annual re:Invent conference, which is taking place virtually this year, Amazon Web Services chief executive Andy Jassy wrapped up with an extended discussion about edge computing and its role in hybrid computing. "Hybrid is not just about whether its on-premise or in the cloud," said Jassy. Instead, IT needs "the same APIs, the same control plane, the same tools, the same hardware they get in AWS regions," said Jassy. He was referring to Amazon's AWS Outposts, a rack of equipment deployed at a customer facility that is a fully-managed service from Amazon. Jassy said Amazon has made the Outposts offering easier to purchase now with new form factors, 1U and 2U rack units, versus an entire rack-size deployment.
Amazon Web Services is adding to its portfolio of business intelligence (BI) services with the preview launch of QuickSight Q, a natural language query tool that functions as a companion feature for its QuickSight cloud service. With QuickSight Q, users can search databases using everyday, natural language and receive an response in seconds, said AWS CEO Andy Jassy during his keynote at the company's annual re:Invent developer conference. Using machine learning and natural language processing, trained over multiple data points and business areas, Q is able to extract business terms (such as revenue, growth, allocation, etc.) and intent from a user's question, surface the related data from the source, and return the answer in the form of numbers and graphs. "We will provide natural language to provide what we think the key learning is," said Jassy. "I don't like that our users have to know which databases to access or where data is stored. I want them to be able to type into a search bar and get the answer to a natural language question."
Marshall McLuhan once famously observed, "First we build the tools, then they build us." Building on artificial intelligence (AI), the Internet of Things (IoT), and 5G communications, advanced software systems are remaking the nature and complexity of human engineering. In particular, digital twin technology can provide companies with improved insights to inform the decision making process. In this day and age, processes and machines are so complex that the risks of failure or disruption from experimenting with different approaches becomes too high or costly. To use an old analogy, it's tough to change the wheels on a moving train. And that can be frustrating when new designs might provide significant benefits to existing systems.
By Johana Moreno, Product Owner The insurance industry has ushered in a new digital era in which customer behavior and preferred interaction is being converted to digital, customers want their problems solved quickly, and new generations prefer services with a rich digital experience. Insurers are looking for more automation and intelligent technologies to address all these trends, which have been exacerbated by the Covid crisis. This is where AI fills the gap and occupies a large space for automation and services with exceptional customer experience. As an AI company, Zelros has been successfully using its machine learning models for various customers, and a wide range of applications in the insurance industry overcoming many challenges. On the one hand, implementing accountable tools to demonstrate the positive impact of our AI models for very demanding customers, and on the other, providing the gold service and reducing time to market. These have allowed Zelros to reinvent and optimize its process without compromising quality. Machine learning is not an insignificant task, behind the scenes all our teams need an important organization to bring high-quality models to production. It requires the combination of two skills Data Science and Devops. AI software editors are facing difficulties to deliver and enhance AI solutions. Actions like data cleaning, data collecting, model training and validation, model deployment, and retraining are most of the time performed manually. This can mislead to operational errors and impacts on productivity and business performance. At Zelros, we believe that culture and environment based on ML technology can bring high business value. Ensuring clear governance for AI lifecycle processes and good automation technology contribute to a robust, transparent, and trustworthy AI. To respond to these challenges, the Zelros platform provides a sustainable cycle for delivering ML into production, a way to orchestrate Data scientists and system integrators activities to work better together, to gain customer confidence and to found our AI solution on transparency and fairness AI principles. Benefits of using our MLOps platform : Benefit 1: Reduce time on data collection and data preparation Data Scientists, systems integrators and solution engineers used to spend a lot of time with repetitive data acquisition or data preprocessing tasks before they could get their hands on the model and use our use cases. However, these tasks were fastidious and costly as many highly skilled resources were allocated before the model was built. MLOps can widely benefit data scientists and software engineers to reduce these operational tasks. We wanted to reduce time connecting customers’ data to Zelros AI platform and to be able to leverage all use cases with fast data connectors. Obtaining up-to-date data is the most important thing to provide a powerful algorithm. For this reason, we paid special attention to data normalization, building and creating a standardized data model that would speed up the deployment process. Normalize data: A standard model is a data architecture where the data is stored, and customers can provide and add information that fits the AI use case. This normalized data provides the capability to use a centralized data environment with all features in one place rather than merging files and overheads from all different data sources every time a new feature is implemented and repeating it for each client. Data scientists can now work on one centralized data environment that respects data protection and data handling policies. Ensure Accuracy: Zelros guides its system in a process cycle in which data is regularly updated, allowing AI to evolve with up-to-date data to ensure the AI model’s response to the behavior and representation of the last population. Data scientists don’t worry anymore about updating data and focus only on model performance. Benefit 2: Automate Model Building (Ready to use) After data scientists and system integrators collected and cleaned up data, they had to manually create, validate and deploy the model. These actions could mislead to errors and lead to overrun in operational costs. To truly create efficiency in operational tasks, training and deployment pipelines need to be automated. Automation can benefit Data scientists to focus on what they do best, extracting business-focused insights, research and looking for innovation and revolutionary techniques to solve AI Ethics issues. The lack of automation was one of the main difficulties; we transformed our traditional pipelines into an AutoML pipeline where our data scientist can simply select the use case and generate a specialized insurance model in a click. This fully automated pipeline continuously trains models resulting in a ready-to-use API. Most of our customers had a long lifecycle for updating their software with the difficulty of upgrading their legacy systems. Besides, every client use case is unique, and the way models’ predictions are used can differ from customer to customer (we do not use data from one client to another). To facilitate interconnections between clients and our platform, Zelros supplies an API collection included on Zelros MLOps automatization pipeline, allowing us to cut the deployment time from 4 to 2 months. Benefit 3: Accelerate the validation process The biggest AI lifecycle challenge is to scale from a small project to a large production system. To move forward, validation tools and transparency are key in the decision-making process, which sometimes requires validation from the business to the legal department. Stakeholders must be able to rely on measurable information before taking the big step. Responsible AI is one of the greatest concerns at Zelros and we pay big attention to this principle. AI automation approach also applies to documentation, and Zelros MLops pipeline includes an Ethical and Fairness report, detailing the AI model in terms of processing, input data, prediction, completeness, behaviors, and other statistical metrics. With a plurality of stakeholders on AI projects, automatic reporting has demonstrated its advantages, such as communication and validation facilitator. The insurance and finance industries are very regulated sectors where decisions made by AI algorithms need to be transparent and follow a strict process. Reporting can facilitate the work between Insurers and external regulators like ACPR or BaFin. For example, […]