Collaborating Authors

How to Use AI & Machine Learning to Make Social Media Marketing Decisions


Northern Light CEO C. David Seuss presented a virtual session at The Market Research Event (TMRE) Digital Week on June 24, about the value of new, AI-driven tools for "decision-oriented analysis" of social media posts to help set and refine an organization's product marketing strategy. Seuss' talk, entitled "Using Machine Learning to Make Social Media Marketing Decisions," focused on analyzing Twitter – the most text content-rich social media platform – for the specific purpose of gleaning business insights valuable to marketing professionals. "Assessing simple co-occurrence of Twitter hashtags is insufficient, and often downright misleading, for marketers of complex products," Seuss asserted in his presentation. "Understanding the context of the social media conversation is vital to derive a truly meaningful analysis of hashtag and keyword overlaps." Seuss explained that using AI and machine learning techniques to measure the semantic similarity of hashtags leads to far more accurate analysis that gets at the importance, from a business perspective, of seemingly related terms.

How the AI hardware market will emerge stronger from 2020 - TechHQ


The semiconductor industry is looking towards recovery strategies. Software has been "the star of high-tech" over the years, but hardware is the core enabler of innovation. As businesses and consumers alike latch on to the advantages of AI applications, whether it's virtual assistants or facial recognition systems, there is a resurging need for advanced hardware. Deloitte describes semiconductors as "essential technology enablers" that power many of the cutting-edge digital devices we use today. By providing next generation accelerator architectures, semiconductor companies can increase computational efficiency or facilitate the transfer of large data sets through memory or storage, crucial for machine learning and AI development.

Stanford 'SIRENs' Apply Periodic Activation Functions to Implicit Neural Representations


The challenge of how best to represent signals is at the core of a host of science and engineering problems. In a new paper, Stanford University researchers propose that implicit neural representations offer a number of benefits over conventional continuous and discrete representations and could be used to address many of these problems. The researchers introduce sinusoidal representation networks (SIRENs) as a method for leveraging periodic activation functions for implicit neural representations and demonstrate their suitability for representing complex natural signals and their derivatives. Traditionally, discrete representations for signals are used when modelling different types of signals in images and videos, processing audio sound waves, performing 3D shape representations via point clouds, etc. The approach can also be used to solve more general boundary value problems such as the Poisson, Helmholtz, or wave equations.

AI For All: The US Introduces New Bill For Affordable Research


Yesterday, AIM published an article on how difficult it is for the small labs and individual researchers to persevere in the high compute, high-cost industry of deep learning. Today, the policymakers of the US have introduced a new bill that will ensure deep learning is affordable for all. The National AI Research Resource Task Force Act was introduced in the House by Representatives Anna G. Eshoo (D-CA) and her colleagues. This bill was met with unanimous support from the top universities and companies, which are engaged in artificial intelligence (AI) research. Some of the well-known supporters include Stanford University, Princeton University, UCLA, Carnegie Mellon University, Johns Hopkins University, OpenAI, Mozilla, Google, Amazon Web Services, Microsoft, IBM and NVIDIA amongst others.

VICE - Detroit Police Chief: Facial Recognition Software Misidentifies 96% of the Time


Several cities have banned police from using facial recognition software, which has well-known racial bias issues (and many false-positive issues as well). Detroit, however, has a very public debate in 2019 about the use of facial recognition, and instead decided to regulate its use rather than ban it altogether. Late last year, the city adopted a policy, which bans the use of facial recognition to "surveil the public through any camera or video device," bans its use on livestream and recorded videos, and restricts (but does not ban) its use at protests. According to the policy, the software must be used only "on a still image of an individual," and can only be used as part of an ongoing criminal investigation. The software checks images across a state database of photos, which include mugshot images.

Learn to analyze and visualize data with Python during this $30 training


As 2020 has clearly shown us, nobody can actually predict the future. But there are some people who come pretty close, and their profession may surprise you. Data scientists (yes, you read that right) can practically predict the future of certain industries using big data and a coding language called Python. Knowing that, it's not hard to see why Glassdoor named data scientists the third most desired job in the US, with over 6,500 openings, a median base salary of $107,801, and a job satisfaction rate of 4.0. If you're looking for a new career path with a handsome salary and the ability to basically predict the future, check out this e-book and course bundle to get started.

Learn to make 3D video games — no coding experience required


TL;DR: Create your own 3D video game with The Complete GameGuru Bundle for $29.99, an 85% savings as of July 2. Every video game fan, at some point, has dreamt of making their own. But, of course, there are quite a few obstacles to such a dream. Fortunately, a lack of programming skills doesn't have to be one of them. With the Complete GameGuru Bundle, even complete coding novices can create their very own 3D video games. Designed for gaming enthusiasts without programming or design expertise, GameGuru is a non-technical and fun game maker that makes the game creation process easy and enjoyable.

Saudi-led coalition hits Houthi-held areas in renewed air raids

Al Jazeera

Fighter jets belonging to a Saudi-led coalition battling Yemen's Houthi rebels have launched dozens of air raids on several Yemeni provinces, as the kingdom announced the start of a new military operation. The Houthi-run Al Masirah Media Network reported air raids on the capital, Sanaa, as well as Marib, al-Jouf, al-Bayda, Hajjah and Saada provinces throughout Wednesday and into the night. It said an elderly woman and a child were killed and four others wounded in Saada province. In Sanaa, residents described the air raids, which also struck the city's international airport, as "violent". Saudi state television reported earlier on Wednesday that the coalition had begun a military push against the Houthis after the group stepped up cross-border missile and drone attacks on the kingdom.

50 Machine Learning and Data Science Companies That Are Revolutionizing Industries


Nowadays it's hard to find a single industry where machine learning and data science aren't being used to improve productivity and deliver results. Indeed that is why people are so excited about the promise of artificial intelligence: it can be applied to so many diverse problem spaces effectively and it works! This list has been aggregated after analyzing over 200 company descriptions, and we've broadly organized them by the problem domain being tackled and have included a brief description of their mission. TLDR: A framework for providing data integrations and web interfaces for trained machine learning models. TLDR: Develops medical imaging tools powered by AI to help improve the efficacy of radiologists in detecting illnesses.

NIO Sets Sales Record In May But Is Still Far Behind Tesla


The Chinese new car market has been topsy turvy lately, primarily because the government keeps playing around with its NEV (new energy vehicle) incentive program. China really, really wants people to buy electric cars -- either plug-in hybrids or battery electrics -- but found its original incentive program was costing too much money. So it modified the program, several times in fact, which caused confusion among car companies and customers. In general, people who are confused postpone buying decisions until things get clearer, and that's exactly what Chinese new car shoppers did. The second factor, of course, was production shutdowns caused by the coronavirus pandemic.