The year 2020 was full of unexpected challenges. Having said that, it also served as a unique opportunity to leverage technology on multiple fronts. From adopting it in various industries such as retail, eCommerce and others, to adopting it to ensure the safety of employees in work from home scenarios, and improving consumer experiences, the industry went through various digital touchpoints. Adoption of data, analytics, AI, cybersecurity and other new technologies saw an exponential growth to bring about changes to fit into the changing business scenario.
Looking at the previous year, 2021 looks like an opportunity for tech trends to grow to newer arenas. Intelligent machines, hybrid cloud, increased adoption of NLP, and overall an increased focus on data science and AI is going to be the highlights in the coming year. Some of the other trends that may see a rise in the coming year are pragmatic AI, containerisation of analytics and AI, algorithmic differentiation, augmented data management, differential privacy, quantum analytics, among others. Considering these trends, it can be said that data is increasingly becoming a critical part of organisations after the pandemic.
The annual data science and AI trends report by Analytics India Magazine aims to highlight the top trends that will define the industry each year. Read More
Monthly Archives: December 2020
China Used Stolen Data to Expose CIA Operatives in Africa and Europe
The discovery of U.S. spy networks in China fueled a decade long global war over data between Beijing and Washington.
Around 2013, U.S. intelligence began noticing an alarming pattern: Undercover CIA personnel, flying into countries in Africa and Europe for sensitive work, were being rapidly and successfully identified by Chinese intelligence, according to three former U.S. officials. The surveillance by Chinese operatives began in some cases as soon as the CIA officers had cleared passport control. Sometimes, the surveillance was so overt that U.S. intelligence officials speculated that the Chinese wanted the U.S. side to know they had identified the CIA operatives, disrupting their missions; other times, however, it was much more subtle and only detected through U.S. spy agencies’ own sophisticated technical countersurveillance capabilities.
… At the CIA, these anomalies “alarmed chiefs of station and division leadership,” said the first former intelligence official. Read More
Tech giants are giving China a Vital Edge in Espionage
U.S. officials say private Chinese firms have been enlisted to process stolen data for their country’s spy agencies.
In 2017, as U.S. President Donald Trump began his trade war with China, another battle raged behind the scenes. The simmering, decade long conflict over data between Chinese and U.S. intelligence agencies was heating up, driven both by the ambitions of an increasingly confident Beijing and by the conviction of key players in the new administration in Washington that China was presenting an economic, political, and national security challenge on a scale the United States had not faced for decades—if ever.
Beijing was giving China hawks in the United States plenty of ammunition. Read More
Top 7 Data Libraries You Will Absolutely Need for Your Next Deep Learning Project
As you know, every machine learning application, including deep learning applications, follows a standard pipeline structure consisting of several steps. Over the years, most of these steps are also standardized in step level for the most part. Since the nature of the workload has become standard, researchers began to build frameworks, which provide solutions for repetitive tasks. The frameworks such as TensorFlow and PyTorch already offers us several modules for all these steps.
Even though deep learning frameworks are very powerful for model building, training, evaluation, and prediction tasks, they fail to compete with specialized complementary data libraries. … The most popular complementary libraries are as follows:
- NumPy for Array Processing
- SciPy for Scientific Computing
- Pandas for Array Pocessing & Data Analysis
- Matplotlib for Data Visualization
- Seaborn for Data Visualization
- Scikit Learn for Machine Learning
- Flask for Deployment
We must stop militant liberals from politicizing artificial intelligence
What do you do if decisions that used to be made by humans, with all their biases, start being made by algorithms that are mathematically incapable of bias? If you’re rational, you should celebrate. If you’re a militant liberal, you recognize this development for the mortal threat it is, and scramble to take back control.
You can see this unfolding at AI conferences. Last week I attended the 2020 edition of NeurIPS, the leading international machine learning conference. What started as a small gathering now brings together enough people to fill a sports arena. This year, for the first time, NeurIPS required most papers to include a ‘broader impacts’ statement, and to be subject to review by an ethics board. Every paper describing how to speed up an algorithm, for example, now needs to have a section on the social goods and evils of this obscure technical advance. ‘Regardless of scientific quality or contribution,’ stated the call for papers, ‘a submission may be rejected for… including methods, applications, or data that create or reinforce unfair bias.’ Read More
2020 in Review: 10 AI Podcasts You Need to Know
The term “podcast” first appeared in the ‘00s, coined by a British journalist as a portmanteau of “iPod” and “broadcast.” Podcasts have since evolved into a popular entertainment and information source, and with 2020 emptying offices and curtailing nights out at the club or cinema, podcasts have become more attractive than ever.
… Synced has selected 10 AI-related podcasts for readers to check out over the holiday season. Read More
Advanced Analytics and AI: Two Divergent But Synergistic Capabilities
Two commonly cited use cases for advanced analytics across financial services entail risk management and fraud detection; specifically, the use of advanced analytics to detect and reduce incidents of false positives.
Many financial services institutions (FSIs) are still working on optimizing these solutions and contrary to what some may believe, artificial intelligence has not rendered advanced analytics obsolete. In fact, many robust AI solutions rely on insights weaned through advanced analytics, and those organizations that are not yet ready to hand off their reins entirely to AI may find solace in mastering advanced analytics first. Read More
DeepMind researchers claim neural networks can outperform neurosymbolic models
So-called neurosymbolic models, which combine algorithms with symbolic reasoning techniques, appear to be much better-suited to predicting, explaining, and considering counterfactual possibilities than neural networks. But researchers at DeepMind claim neural networks can outperform neurosymbolic models under the right testing conditions. In a preprint paper, coauthors describe an architecture for spatiotemporal reasoning about videos in which all components are learned and all intermediate representations are distributed (rather than symbolic) throughout the layers of the neural network. The team says that it surpasses the performance of neurosymbolic models across all questions in a popular dataset, with the greatest advantage on the counterfactual questions. Read More
Joint Artificial Intelligence Center (JAIC) Brief
Here’s Why Quantum Computing Will Not Break Cryptocurrencies
There’s a lurking fear in cryptocurrency communities about quantum computing. Could it break cryptocurrencies and the encryption that protects them? How close might that be? Do the headlines around “quantum supremacy” mean that my private keys are at risk?
The simple answer: no. But let’s dive deeper into this phenomenon and really try to understand why this is the case and how quantum computing will interact with cryptocurrencies. Read More
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