The business world has started to recognise that for AI and IoT to be successful, they must be mutually beneficial ecosystems. An IoT device is connected directly or indirectly to a data-transmitting network such as the internet. The optimum value one can get from connected devices is some level of automated, enriched, intelligent insights. This “artificial intelligence” must logically become more valuable than the sum of its constituent, connected parts just like in an ecosystem.
Artificial Intelligence (AI) is finally making headways in the broader Customer Interaction Management space. Customer service departments have a lot of technology options to choose from to better their productivity and the customer experience. It incentivizes them to invest in software allowing incremental improvement of performance indicators. This has led to a conservative approach with breakthrough technologies such as AI. This is changing through the state of AI adoption.
We know the data is there, and we want to leverage it. By the time we get our hands on it, the opportunity has passed, and the information is too out-of-date to lend any real relevance. So the data sits there, in the dark, never becoming the insight we need to make critical business decisions. All the while, the volumes of data grow, threatening to shroud dark data in deeper, permanent obscurity. For this reason, AI gives us both access to information and the time to leverage it for business advantage.
Manufacturers and utilities are already tracking millions of data streams and generating terabytes a day. But everyone wants different cuts of data. A lot of those users sit near the data asset, so it makes sense to keep the data there. The best bet is to look at the use case scenario first. Chances are, every workload will require both cloud and edge technologies, but the size of the edge might be larger than anticipated.
Blockchain technology can address current inefficiencies within the supply chain space, bringing new levels of traceability to logistics processes hindered by current paper-based solutions. Using blockchain’s distributed and decentralized ledger, records of transactions can’t be erased, boosting overall transparency, increasing efficiency and improving cash flow for logistics operators. It would be wise to focus on use cases that involve multiple parties using transactions to synchronize ledger information and use cases that requires immutable records — both exciting challenges that demands the major industry players to collaborate and create a solid foundation for a community.
There are eight things that really matter if you want to prosper in leveraging the Internet of Things (IoT) to drive innovation. An IoT solution that includes all eight of these elements enables enterprises – no matter what their size – to accelerate their IoT adoption easily, quickly and at scale. Here are the eight features to look out for when evaluating IoT solutions.
Neural networks are a ground breaking technology. Like machine learning in general, they are becoming more and more integrated into our lives without many of us realising. They can be wildly complex and it is very difficult to understand how they come to decisions. As the name suggests the inspiration behind their design comes from something we all own and use every day; the brain.
Had Newton known about machine learning and had the actual machines to do the learning, then this is how he might have gone about it. First, he could have set up a classification problem with three class labels “down”, “up” and “sideways”. Then he would collect data on the direction of falling apples. He would have noticed his dataset to be highly imbalanced. But, undaunted, he would have marshalled on and trained his classifier. If his classifier were any good it would predict “down” as the direction of fall in most cases.
Data Scientists must communicate effectively, and need to use Ethos, Logos, and Pathos. Ethos establishes the credibility of the speaker, and Logos appeals to the reasoning used. Yet, both are useless without Pathos, the way to the heart of the audience. The Data Scientists need data-driven reasoning when presenting their work. This is generally the right way to argue. But to evoke actions, Data Scientists need to tell stories.
Reference data is a non-volatile and slow-moving subset of enterprise data. It is often standardized by external bodies and businesses generally use the same reference data throughout their operations. Examples include country codes, SIC codes, currencies and measurement units. Reference data anchors a company’s data initiatives, and maintaining consistent, high-quality reference data is essential to successful data management. A sound RDM solution provides enterprise-wide benefits, including lower maintenance costs, greater operational efficiencies, more accurate analytics, reliable data governance and full compliance.
Scrapy is a framework built to build web scrapers more easily and relieve the pain of maintaining them. Basically, it allows you to focus on the data extraction using CSS selectors and choosing XPath expressions and less on the intricate internals of how spiders are supposed to work. If you need to scrape something a bit harder, you can do it on your own. With that, let's get started.
When you think of the perfect data science team, are you imagining 10 copies of the same professor of computer science and statistics, hands delicately stained with whiteboard marker? Applied data science is a team sport that’s highly interdisciplinary. Diversity of perspective matters! In fact, perspective and attitude matter at least as much as education and experience. If you’re keen to make your data useful with a decision intelligence engineering approach, here’s my take on the order in which to grow your team.
Companies are sitting on some of the world’s largest reservoirs of valuable data, but many of them aren’t doing anything with it. It’s pretty hard to “do anything with it” if the data storage method doesn’t allow one dataset to talk to another. Or if tools like Hadoop and Spark process large amounts of data in a distributed fashion using commodity hardware”—can’t readily access said data to work their magic on it. Legacy institutions suffer from infrastructures predating the so-called information age. Yet these siloed institutions are the ones with the most to gain from the efficiencies and insights that data can bring.
One aspect that seems to be conspicuous by its absence is product definition of AI powered software. The reason is not too difficult to fathom. Defining AI products is hard, and there are no industry standard methodologies to speak of. As with any new paradigm shifting technology, it takes some time before the business processes can catch up. AI is no different. In the long run, I believe that a basic understanding of AI within the wider product development community is the solution.
Look at the fundamental building blocks for a flexible presentation of data. The real power of this concept lies in uncaging your data from the confines of monolithic charts and setting them free, to tell their own expressive story. Though many visualization tools today don’t adopt a grammar of graphics approach in its entirety, that seems to be the way forward. Meanwhile, there are opportunities for people to start putting this to practice. This is so important that it must be made mandatory education for anyone working with data, whether it is analysts, designers, data scientists or journalists.
Assistants and bots have reached a new adoption high. However, many businesses are finding their projects harder to scale than they expected. The disappointment with some deployments triggered controversy whether to use Artificial Intelligence (AI)-powered assistants or to stick with rule-based bots. Assistants and bots are relatively easy to set-up. Companies can start rapidly but are finding it more difficult to scale and can become disappointed. Challenges maximizing conversational AI should not be construed as the technology not being ready for large deployments. Let’s explore what needs to be done to get the most out of conversational AI.
Real world data science model is at the heart of our hedging business risk and increasing customer satisfaction. The ecommerce space is going through a major transformation like the Fintech industry, where one-to-one marketing and personalization are driving innovations through technologies like machine learning. Companies are now able to leverage this fascinating convergence of technology and consumer demand. Attaining personalized engagement with your consumer has to be on top of the list for every marketer in today’s crowded marketplace.
What exactly is data science? Data is to Data Science, as Elements are for Chemistry. The most basic thing you can have in Data Science is a data point. From data, data scientists can build a model to explain what is going on in the scenario we’re facing, validate it, and test it. But to do all this, we need a little bit of Computer Science, Math and Statistics, and Domains / Business Expertise.
Most of the AI in use now is narrow AI, meaning it is only capable of performing individual tasks. Narrow AI does a good job at executing tasks, but it comes with limitations, including the possibility of introducing biases. AI bias may come from incomplete datasets or incorrect values. Bias may also emerge through interactions overtime, skewing the machine’s learning. Moreover, a sudden business change, such as a new law or business rule, or ineffective training algorithms can also cause bias.
The Internet of Things and artificial intelligence are deeply connected. IoT systems produce big data, whereas, data is the heart of AI and machine learning. At the same time, as the rapid expansion of connected devices and sensors continues, the role of smart technologies in this space is growing too. Today, the applications of computer intelligence in IoT products vary. In this article, I’d like to focus on a specific domain of AI – Natural Language Processing.
How can machine learning help theoretical science? Machine learning can provide the mathematical scaffolding for scientific theories, to which theorists will then add meaning and the bridge to reality. However, before we can get there we will need to develop a much better understanding of machine learning. We will need to understand machine learning algorithms from general principles. Perhaps, it is time to start developing a real theory of machine learning.
Like any automation process, the most basic expectation from salesforce automation (SFA) tools is to improve efficiency and effectiveness of sales operations. Yet when you look closely, salesforce automation is usually a feature within your CRM or ERP tools to track and manage selling activities and reports, which is just not enough because not all selling are equal. While the sales numbers are not completely owned by marketing, the insights from marketing operations are a key component of sales force automation.
There is a paradigmatic shift in the way digital is becoming part of our life: from digital payments, to the way we shop or even interact with each other. Today, there are millions of connected devices, smartphones, as well as Internet users. Such is the extent of this digital revolution. This is bound to have far-reaching implications for organizations as far as cyber security is concerned. Artificial Intelligence can provide the requisite sharpness to ward off IoT security issues.