Enterprises worldwide have shown us repeatedly that there is major potential for industry change with ML and AI. Nevertheless, in order to bring about that change, there must be strategy involved. It is not enough to assemble a large data team and expect the results to come. Becoming successful with data requires collaboration across teams, placing new concepts – such as reusability and reproducibility of data models – at the heart of the business.
A significant transformation is occurring in the back offices of enterprises across the globe. Many label it as the rise of the robots, but a more appropriate term is robotic process automation (RPA). RPA brings forth technology that combines scripting with intelligence and execution. It is a combination of automated prowess that has been a real boon for back-office operations.
This year, cybercriminals caused major service disruptions around the world, using their increasing technical proficiency to break through cyber defenses. In 2018, we expect the trend to become more pronounced as these attackers will use machine learning and artificial intelligence to launch even more potent attacks.
Some of the AI deployments we might get to see in lesser than a decade are autonomous driving, data-driven preventive maintenance, powerful surveillance and monitoring systems for transportation and pedestrians, sustainable mobility, advanced traveler infotainment systems and services, emergency management systems, transport planning, design and management systems and at last but not the least, environmental protection and quality improvement systems.
The global IoT market will grow from $157B in 2016 to $457B by 2020, attaining a Compound Annual Growth Rate (CAGR) of 28.5%. Bain predicts B2B IoT segments will generate more than $300B annually by 2020, including about $85B in the industrial sector.
Wherever you have a long list of data and need to perform some mathematical transformation over them, strongly consider turning those python data structures (list or tuples or dictionaries) into numpy.ndarray objects and using inherent vectorization capabilities.
Many may agree that data is in fact the world’s most valuable resource, but the numbers show that data is actually the world’s most underused valuable resource. The truth is that only 1-5% of all data is actually used. It might be easy for companies to pat themselves on the backs about the valuable data that they are collecting, but if it’s not being used, then what’s the point? Based on the numbers, companies could make decisions that are 95-99% better if they just used the data they already have.
RPA platforms, in a way the most visible manifestations of the move towards automation that enterprises are getting to hear about. The kind of variability between the RPA platforms makes the task of predicting what lies ahead for them in 2018 difficult. However, our attempt at this prediction is based on what we are hearing from our clients and also the strategies that we are seeing some of our product partners adopt.
There has been a lot of talk about fintech lately. We talk about the billions of dollars being invested in fintech; the wave of unicorns and start-ups in this space; the challenge they bring to banks and incumbents; the way in which they are reaching new spaces and places; but what is fintech? It is no longer this big bucket of finance and technology. In fact, saying ‘fintech’ is like saying ‘retailer.’ But what exactly are they retailing and, in the fintech sense, what areas of finance are these companies automating?
Being different can be good. In fact, being Different can be great, and that is the whole idea of having a differentiator for your business. If you truly have such a differentiator, I actually encourage you to use buzzwords when the time is right. Just like using “Cloud,” saying “My sensors use proprietary machine learning on the Edge” conveys an incredible amount of information, uses less than ten words to do so, and has saved both of us a lot of time in the process – and is thus incredibly efficient.
Missing data are probably the most widespread source of errors in your code, and the reason for most of the exception-handling. If you try to remove them, you might reduce the amount of data you have available dramatically — probably the worst that can happen in machine learning.
With this paper, we provide a brief analysis of the most notable differences between the distributed ledger technologies (DLT) Hyperledger Fabric, R3 Corda and Ethereum. Our intention is to give decision makers new to DLT guidance for what use cases Hyperledger Fabric, Corda and Ethereum are most suitable.
Data has a story to tell at every phase of the product lifecycle. It can predict demand, inform the development and optimize distribution. But that’s only half of the tale: With the right tools, brands can continue to glean valuable insight from data once products leave the store shelves. Big data has a complete story to tell — if brands are listening.
Big data is, or soon will be, a reality in professional life for the vast majority of people in the modern economy. Here I’ve explored just three strategies you can use to help yourself be successful in that world. 1984 wasn’t just the year of Big Brother, it was the underwater tremor of what would—by 2017 —become an earthquake of data. The tsunami is on its way.
Deep Learning has generated the biggest breakthroughs over the last five years. You can almost always download a “pre-trained” model and apply it to your data — for example, you can download pre-trained image classifiers that you can feed your data through to either classify new images or draw boxes around the objects in images. Because much of this work has been done for you, the work necessary to use these cutting edge techniques is not in “doing the deep learning” itself — the researchers have largely figured that part out for you — but rather in doing the “dev” work to get the models others have developed to work for your problem.
FinTechs are dramatically changing the perception and function of banking. Banks find themselves having to transform into trusted brands and get to know their customers anew by getting close to their needs and understanding their working style. They also need to start providing real help in their areas of greatest need. Also, besides merely focusing on meeting the customer’s financial needs, they should additionally offer real-time, on-demand financial troubleshooting.
When it comes to big data, challenges derive from the nature and the volume of the data. Whether it is a data leak or a financial company’s internal data, the amount of data we are dealing with is considerable. To complicate things, investigations usually start from raw, unstructured data. And it’s impossible to automate or scale the investigation without a predefined-data model or any kind of organizational logic.
With blockchain-based technology like bitcoin taking up news headlines, awareness and excitement about other potential uses for blockchain is increasing. One of the best use cases for blockchain may end up being healthcare and medical records. By having a distributed database for healthcare-related information, healthcare providers can benefit from increased accessibility, accuracy, and safety, all of which will result in better healthcare outcomes for all.
Today’s telecommunication operators are facing greater competition and increasing challenges within the marketplace. That means that everyone needs to find a new way of minimising costs, and enhancing revenue if they want to succeed. When it comes to performance, the blockchain has a lot to offer, including better trust and transparency. The distributed nature of the blockchain means that there are no single points of failure, no worries about hacking attacks, and no stress caused by control from a single entity.
The marketing and advertising industries are going through a paradigm shift as technology progresses exponentially. It was not long ago when big data was the talk of the town. The challenge was in collecting and manipulating it. In the next three years, machine learning, Artificial Intelligence (AI) and the Internet of Things (IoT) will force us to deal with a large amount of data. The more people are going to use technology the more they will leave a footprint data, that will be available for marketers and advertisers to be leveraged for gaining deep insights.
The definition of Big Data will continue to change. And, “Big” today is very different than it was just a few years ago. As technology progresses, the definition of what is being collected as big data will change. No matter what your definition of Big Data is, when it comes to a conversion project the keys to success are having a project plan, and the right people to work with.
Data now informs organisations about trends and problems they never knew existed. It shapes how people interact, share information, purchase goods, and how they’re entertained and how they work. It dictates political decisions and economic cycles. Data is the raw power that helps us optimise decisions and processes to iron out inefficiencies through use of analytics. Analytics can be utterly transformative.
Blockchain adds some important elements to the peer-to-peer network. This allows the participants to verify and audit transactions. The network relies on mass collaboration driven by the shared interests of the participants, and the result is (or should be) a shared data set where there is little if any, uncertainty regarding data security.
With all the talk on “the street” about disruption in the various markets, from the auto industry to retail, it can be easy to act reactively and change too fast too soon rather then to be strategic and do what is best for your line of business