Dataconomy
MAY 31, 2016
The typical Hollywood portrayal of spywork makes the field appear a lot more glamorous than it really is. For better or worse, intelligence agencies really feature a lot of sorting through files, documents, numbers, and other data, most of it done in office buildings with employees hunched over computers. While. The post The Growing use of Big Data at Intelligence Agencies appeared first on Dataconomy.
Explosion
MAY 12, 2016
Yesterday, Google open sourced their Tensorflow-based dependency parsing library, SyntaxNet. The library gives access to a line of neural network parsing models published by Google researchers over the last two years. I've been following this work closely since it was published, and have been looking forward to the software being published. This post tries to provide some context around the release — what's new here, and how important is it?
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Dataconomy
MAY 27, 2016
Big Data includes so many specialized terms that it’s hard to know where to begin. Make sure you can talk the talk before you try to walk the walk. Data science can be confusing enough without all of the complicated lingo and jargon. For many, the terms NoSQL, DaaS and. The post A Beginner’s Guide to Big Data Terminology appeared first on Dataconomy.
Dataconomy
MAY 23, 2016
Finding missing children and unraveling the complex web of human trafficking is no easy task. The relevant datasets are massive and often unstandardized. It can be difficult to find the right data at all, as it often disappears from websites and pages on a regular basis. When data is hard. The post Data Science Leveraged to Stop Human Trafficking appeared first on Dataconomy.
Advertisement
Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?
Dataconomy
MAY 17, 2016
Machine-learning is all the rage in fraud detection, with industry analysts, academics, businesses and technology media examining the advantages of algorithms and big data in the fight against e-commerce fraud. Especially for fraud analysts working in companies with small budgets , machine-learning tools are seen as a cost-effective way to. The post Machine Learning and Fraud: Why Artificial Intelligence Isn’t Enough appeared first on Dataconomy.
Dataconomy
MAY 20, 2016
Developers are gatekeepers to the future of possibilities enabled by the Internet of Things. Billions of new devices will go online and connect to the cloud by 2020, from simple sensors to smart light bulbs, connected machinery and the gateways managing all those connections. Developers are responsible for equipping each. The post Why IoT developers need open source framework appeared first on Dataconomy.
Data Science Current brings together the best content for data science professionals from the widest variety of thought leaders.
Dataconomy
MAY 16, 2016
The future of 3D printing is heavily reliant on the power of data. The Internet of Things means users will be able to access and print files remotely, as well as create incredible scans and share prints. But, there are also several more opportunities for big data and 3D printing. The post Three Unexpected Uses for 3D Printing in Big Data appeared first on Dataconomy.
Dataconomy
MAY 2, 2016
The famous inventor and computer scientist Ray Kurzweil has made some very bold predictions about the pace at which human technology is advancing toward the ultimate threshold. That threshold is known as “The Singularity.” That epithet is a metaphor borrowed from physics terminology to express the point at which information. The post How Far Away Are We from Inventing True A.I.?
Dataconomy
MAY 26, 2016
Serial entrepreneur Georg Ludviksson co-founded Meniga in 2009in the wake of the global financial crisis in Iceland. Georg has spent 20 years founding, building and managing global software start-ups. Georg holds an MBA degree from Harvard Business School with emphasis on Entrepreneurship and Finance. He also holds a BS degree. The post “We believe that personalization is the key word for FinTech this year”-Interview with Meniga’s Georg Ludviksson appeared first on Dataconomy.
Dataconomy
MAY 26, 2016
Serial entrepreneur Georg Ludviksson co-founded Meniga in 2009in the wake of the global financial crisis in Iceland. Georg has spent 20 years founding, building and managing global software start-ups. Georg holds an MBA degree from Harvard Business School with emphasis on Entrepreneurship and Finance. He also holds a BS degree. The post “We believe that personalization is the key word for FinTech this year”-Interview with Meniga’s Georg Ludvikkson appeared first on Dataconomy.
Advertisement
Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.
Dataconomy
MAY 6, 2016
Is beauty in the eye of the beholder? Data scientists aren’t so sure. Many consider the Turing test to be the ultimate judge of whether artificial intelligence is fully developed. Technology should be able to behave in such nuanced human ways that even humans recognize it as human. That’s one. The post Better than Botox: Beauty Industry To Get an AI Makeover appeared first on Dataconomy.
Dataconomy
MAY 24, 2016
Big Data. It’s everyone’s favourite buzzword. The Big Data trend has the potential to revolutionise the IT industry by offering businesses new insight into the data they previously ignored. For many, it is seen as the Holy Grail for businesses today. For organisations, it’s the route towards better understanding exactly. The post Big Data isn’t the problem – data copies are appeared first on Dataconomy.
Dataconomy
MAY 30, 2016
No matter what word you use—virtual reality, augmented reality, artificial life, virtual environments—the upcoming family of virtual technology is slowly but surely coming to the general market. New VR companies are popping up. Old companies are finding new uses for VR. Data science is getting a VR makeover. All of. The post An Introduction to Virtual Reality: Where Does the Technology Stand?
Dataconomy
MAY 3, 2016
FinTech is disrupting the banking world—and bringing consumers along for the ride. Of course, such a major shift in policies and the way companies do business is bound to have equally powerful effects on legislature. Regulations have been strict since 2008, and many US banks have been hit with huge. The post RegTech: The 2016 Buzzword Is Turning Heads appeared first on Dataconomy.
Speaker: Scott Sehlhorst
We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.
Dataconomy
MAY 10, 2016
How drones, sensors, and even Google Glass are making news better. The Internet of Things is set to disrupt media like never before. Marketing and advertising will be reborn, understanding and reaching consumers with an unprecedented degree of precision. That is not, however, the only way media will be changing. The post Empowering Journalists with the Internet of Things appeared first on Dataconomy.
Dataconomy
MAY 13, 2016
Finding and hiring a top-notch data scientist is a tough endeavor. The distinct skill set at the intersection of mathematics, statistics, information technology and business is rare to find and suitable candidates are well aware of their market value. Salary expectations well beyond $100,000 dollars are the new normal. Nevertheless, The post WAIT – Is this person even a data scientist?
Dataconomy
MAY 12, 2016
Despite some ups and downs over the years, Bitcoin is still hanging round as an alternative currency. There are those who still believe that the future is bright, and that Bitcoin may just represent the future of money. Indeed, we’re still pretty early in the life of the leading cryptocurrency. The post Bitcoin Usage and The Future Of The Blockchain appeared first on Dataconomy.
Dataconomy
MAY 19, 2016
Cosmin Ene is an entrepreneurial founder with an excellent first-hand understanding of the life cycle of entrepreneurial ventures, which he has accumulated over 18 years. Between 2005 and 2009, Ene was co-founder and managing director of DELUXE Television. Prior to this, he was analyst and associate at TecVenture Partners. In. The post “I expect the market to become much more fragmented as some areas of FinTech”… – Interview with LaterPay’s Cosmin Ene appeared first
Speaker: Timothy Chan, PhD., Head of Data Science
Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.
Dataconomy
MAY 11, 2016
Darya Niknamian Content Marketing Manager Originally born in Germany but grew up in Canada (the land of sorry’s and eh’s) Calling herself a Berliner for just over a year Thinks life is not complete without a little daily sarcasm What brought you to Dataconomy in the first place? I have. The post Behind the Scenes of Dataconomy: Meet Darya!
Dataconomy
MAY 25, 2016
Elena Poughia Managing Director Originally from Greece (the country with 6,000 islands) Calling herself a Berliner for just over a year Her motto “When there is will, there is a way” as she believes life is about hustling with a purpose (ideologist at heart) but really, in it for the ride. The post Behind the Scenes of Dataconomy: Meet Elena! appeared first on Dataconomy.
Dataconomy
MAY 5, 2016
Mike Maciag is the COO of Altiscale. Prior to Altiscale, he served as the president and CEO for DevOps leader Electric Cloud, where he grew the revenue from zero to tens of millions while building a worldwide presence and signing hundreds of blue-chip customers. Mike holds an MBA from Northwestern. The post “Hadoop practitioners alike should rejoice in the rise of Spark…”- Interview with Altiscale’s Mike Maciag appeared first on Dataconomy.
Explosion
MAY 12, 2016
Yesterday, Google open sourced their Tensorflow-based dependency parsing library, SyntaxNet. The library gives access to a line of neural network parsing models published by Google researchers over the last two years. I’ve been following this work closely since it was published, and have been looking forward to the software being published. This post tries to provide some context around the release — what’s new here, and how important is it?
Speaker: Kevin Kai Wong, President of Emergent Energy Solutions
In today's industrial landscape, the pursuit of sustainable energy optimization and decarbonization has become paramount. Manufacturing corporations across the U.S. are facing the urgent need to align with decarbonization goals while enhancing efficiency and productivity. Unfortunately, the lack of comprehensive energy data poses a significant challenge for manufacturing managers striving to meet their targets.
Explosion
MAY 10, 2016
In v0.100.3, we quietly rolled out support for GIL-free multi-threading for spaCy's syntactic dependency parsing and named entity recognition models. Because these models take up a lot of memory, we've wanted to release the global interpretter lock (GIL) around them for a long time. When we finally did, it seemed a little too good to be true, so we delayed celebration — and then quickly moved on to other things.
Explosion
MAY 10, 2016
In v0.100.3, we quietly rolled out support for GIL-free multi-threading for spaCy’s syntactic dependency parsing and named entity recognition models. Because these models take up a lot of memory, we’ve wanted to release the global interpretter lock (GIL) around them for a long time. When we finally did, it seemed a little too good to be true, so we delayed celebration — and then quickly moved on to other things.
Explosion
MAY 8, 2016
Many people have asked us to make spaCy available for their language. Being based in Berlin, German was an obvious choice for our first second language. Now spaCy can do all the cool things you use for processing English on German text too. But more importantly, teaching spaCy to speak German required us to drop some comfortable but English-specific assumptions about how language works and made spaCy fit to learn more languages in the future.
Explosion
MAY 8, 2016
Many people have asked us to make spaCy available for their language. Being based in Berlin, German was an obvious choice for our first second language. Now SpaCy can do all the cool things you use for processing English on German text too. But more importantly, teaching spaCy to speak German required us to drop some comfortable but English-specific assumptions about how language works and made spaCy fit to learn more languages in the future.
Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage
Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.
Expert insights. Personalized for you.
132 
Let's personalize your content