With more and more data being collected on every transaction between individuals and organizations, companies and consumers everywhere are becoming increasingly concerned with security. Going by the numerous data breaches that have filled newsfeeds in the past few years, it is easy to understand why customers are wary about the safety of their private information.
The fact of the matter is that big data is not going away anytime soon. 85% of businesses have reported that data-driven systems have become an integral part of their company, but nearly half have reported issues with data security in the past. It is clear that customers are quite aware of the shortcomings in data security as well.
No matter the industry, studies show that the majority of customers feel less than confident with the privacy and security of their personal data, and very few feel confident at all in the safety of their information.
Obviously, it can be quite difficult to convince someone to buy from you if they do not trust your business. You must be able to communicate in no uncertain terms that your big data systems put customers’ information to good use, and keep it safe at all times. Whether it is their ...
domination of markets everywhere. The data revolution that’s fundamentally reshaped the economy from the top down has overwhelmingly been a net positive, however, and for all the disruption that big data recently introduced to the market, businesses everywhere are coming to love it for the savings it can bring them.
Few sectors have benefited from the rise of big data more than the financial and banking sectors, which are starting to employ data analytics with much more gusto than many other industries. So, how exactly is big data changing the faces of these vital industries, and what can professionals within them do to guarantee their futures in an uncertain time?
Don’t fear big data – exploit it
The first thing that you need to understand about big data is that, when it comes to the banking and finance sectors, big data hasn’t been feared and avoided, but rather embraced with zeal. Across the market and a myriad of industries, when working professionals here terms like artificial intelligence, machine learning, and of course big data analytics, they instantly begin to worry about losing their job to automation. The reality on the ground, however, is that recent tech trends have opened up many more ...
It is an outrageous scandal that we should not accept. Even if your Facebook profile has not been obtained by Cambridge Analytica, we should not accept such a massive breach of privacy, and those responsible should be held accountable. Those valuing their privacy should think carefully about their usage of Facebook.
For those unaware of Cambridge Analytica, it is a data mining and data analysis company that has played a pivotal role in the US Presidential Election of 2016, the Brexit vote and many other political races. Behind the company are key figures of President Trump, including Steve Bannon, Trump’s former strategic advisor, and Robert Mercer, founder of the Government Accountability Institute that uses the dark web and bots to denigrate political opponents.
In 2014, the company used personal information obtained without the authorisation of the users to develop a highly effective system to target individual US voters. Under the pretence of academic research, they harvested 50 million profiles without those users approving of being harvested and then used that data to influence the US election. It is a privacy breach at an unprecedented scale, and it shows that Facebook’s attempts to protect their users are not working.
In memory of Alan Turing, Marvin Minsky and John McCarthy
Every decade seems to have its technological buzzwords: we had personal computers in the 1980s; Internet and worldwide web in 1990s; smart phones and social media in 2000s; and Artificial Intelligence (AI) and Machine Learning in this decade. However, the field of AI is 67 years old and this is the second of a series of five articles wherein:
The first article discusses the genesis of AI and the first hype cycle during 1950 and 1982
This article discusses a resurgence of AI and its achievements during 1983-2010
The third article discusses the domains in which AI systems are already rivaling humans
The fourth article discusses the current hype cycle in Artificial Intelligence
The fifth article discusses as to what 2018-2035 may portend for brains, minds and machines
The resurgence of Artificial Intelligence
The 1950-82 era saw a new field of Artificial Intelligence (AI) being born, a lot of pioneering research being done, massive hype being created, and AI going into hibernation when this hype did not materialize, and the research funding dried up . During 1983 and 2010, research funding ebbed and flowed, and research in AI continued to gather steam although "some computer scientists and ...
The healthcare industry is on the edge of a revolution. Thanks to different advances in technology, including the IoT, big data, and genomics, doctors and healthcare professionals are going to be able to better treat individual patients. It’s possible now to both monitor the lifestyles of patients and have a better understanding of their genetics and what extra health risks they have.
What is Genomics?
Genomics is the study and mapping of genomes and the DNA within. Every person has a unique set of genomes that makes up who they are. By mapping out a person’s genome, doctors and scientists can get a better understanding of why they have certain physical traits and find out what genetics they got from each parent.
By combining genomics with big data sources, the field is now able to do amazing things. Since every person’s genomes are unique, the data isn’t very useful for diagnosing and improvement without being able to compare it to others. With ever-growing databases of genomes though, healthcare data scientists can compare a person’s genomes to millions of others and find patterns in their genetics and what implications specific genes have for their health.
By using genomics and big data together, it’s possible ...
Most commercial deep learning applications today use 32-bits of floating point precision (ƒp32) for training and inference workloads. Various researchers have demonstrated that both deep learning training and inference can be performed with lower numerical precision, using 16-bit multipliers for training and 8-bit multipliers or fewer for inference with minimal to no loss in accuracy (higher precision – 16-bits vs. 8-bits – is usually needed during training to accurately represent the gradients during the backpropagation phase). Using these lower numerical precisions (training with 16-bit multipliers accumulated to 32-bits or more and inference with 8-bit multipliers accumulated to 32-bits) will likely become the standard over the next year, in particular for convolutional neural networks (CNNs).
There are two main benefits of lower precision. First, many operations are memory bandwidth bound, and reducing precision would allow for better usage of cache and reduction of bandwidth bottlenecks. Thus, data can be moved faster through the memory hierarchy to maximize compute resources. Second, the hardware may enable higher operations per second (OPS) at lower precision as these multipliers require less silicon area and power.
In this article, we review the history of lower numerical precision training and inference and describe how ...
Big data is becoming even more important for businesses, in practically all areas of business development and management. By the end of 2017, 53 percent of companies were either using or intending to use big data for major decisions, with increased representation in telecom and financial services industries.
But data isn’t just about making more accurate financial projections or understanding how your customers are using your products; it’s also about understanding the best way to reach and communicate with your customer base.
The prevalence and prioritization of data is already starting to reshape brand messages, and that transformation is only going to get more pronounced from here.
Data is starting to redefine the most important concepts, priorities, and strategies for an overarching campaign:
The role of analysis. Marketing departments that were once flush with creative types and visionaries are now bringing in more data analysts—a position that’s experiencing high growth over the past few years. Data analysis is becoming more crucial for success, and businesses are recognizing it by rebalancing their teams and bringing in the experts capable of asking the right questions.
Audience targeting. Data is also helping companies recognize who their “real” target audience is, and what that audience likes to see. ...
Over the past few years, I have repeatedly written about the endless benefits of big data. However, I don’t write enough about the problems that transpire when big data projects are improperly managed.
A few years ago, a colleague of mine operated a company that was trying to develop a variety of AI applications for startups in the Bay Area. Due to the sophisticated nature of the problems that he was trying to solve, he relied extensively on big data. He and his team members were very knowledgeable data scientists and web developers, so they were certain that the solutions they developed would be highly useful to their future clients.
They were in for a rude awakening when they finally pitched to a growing company in San Francisco. They had a presentation with the company and showed a demonstration of the final application. Unfortunately, the company swiftly rejected the proposal. The harsh reply indicated that other potential clients would probably be equally disinterested.
My colleague’s team reached a disheartening conclusion. They developed a poorly thought out big data application that nobody would purchase. Five months of their lives and over $10,000 was wasted.
What went wrong? The biggest mistake they made was delegating all ...
With consumer data privacy becoming a top priority in the current age, regulating authorities have jumped into the conundrum to ensure that users get the privacy they need for their personal data. One such regulatory authority that has come into the mix to ensure rights for all users online is the European Union. The EU announced the General Data Protection Regulation or GDPR, that will be in full effect by May of this year. Although GDPR may be considered a regional regulation, its impact is far-flung and may be seen across the globe in the coming days.
While GDPR imposes regulations on many aspects of management and user protection, the main clause of the regulation is that users will now be able to control their own personal data online and organizations will be required to protect the data that users share with them. New protection methods for personally identifiable information or PII gives every EU citizen the right to approve the use of their personal data. Citizens can now allow the use of their data or can opt for the “right to be forgotten” as an alternative.
The enforcement of the GDPR by the EU will be done through the implementation of ...
Businesses of all sizes should be adequately prepared to invest in the infrastructure and resources to collect and analyze big data or obtain it by other means.
Big data is not simply that avalanching amount of data you aggregate in your operations. In the near future, it will become a necessity to compete effectively. However, there is a slight misconception about startup companies and big data.
It is often assumed that startups are unable to take advantage of big data because they have a smaller pool of data in comparison to larger companies. This is not true, as there are plenty of ways startups can also get involved in big data, too.
Here are a few options for startup companies to take advantage of big data to improve both their operations and customer experience.
Purchase the Data You Lack
Ideally, startups should use their own data where possible as it reflects their own actions and choices, but sometimes the pool of data on hand is just very small to work with. In that case, in order to understand your industry and audience better, it might be a good idea to purchase data from other companies or take advantage of free online datasets.