Artificial Intelligence is slowly taking over given its ability to enhance efficiency in various operations.

In 1951, Marvin Minsky asked an imaginary mouse to navigate an imaginary maze. Together with Dean Edmonds, Marvin carefully connected three hundred vacuum tubes together with an assembly of motors and light bulbs, applying ideas about the wiring of neurons in human and animal brains. Minsky and Edmonds watched the virtual mouse's progress on a bank of lights and offered rewards when it moved toward its goal. Through repeated tries, the mouse learned to escape the maze. When researchers coined the term "artificial intelligence" (AI) five years later, they hoped to prove in one summer that every feature of learning and intelligence could be conducted by a machine.

Apple says it will no longer retain audio recordings of your interactions with Siri by default and has issued an apology for having done so previously. "We realize we haven't been fully living up to our high ideals, and for that we apologize," Apple stated on its website. Apple had taken a lot of heat for the practice recently following a report by the Guardian that contractors "regularly hear confidential medical information, drug deals, and recordings of couples having sex, as part of their job providing quality control, or grading'" Siri. "We know customers have been concerned … We heard their concerns," the company wrote. Apple had previously announced that it halted the grading practice, which, following software updates and other changes, will resume in the fall.

When comes to machine learning, data is certainly the new oil. The processes for managing the lifecycle of datasets are some of the most challenging elements of large scale machine learning solutions. Data ingestion, indexing, search, annotation, discovery are some of the aspects required to maintain high quality datasets. The complexity of these challenges increase linearly with the size and number of the target datasets. While it is relatively easy to manage training datasets for a single machine learning model, scaling that process across thousands of dataset and hundreds of models can become nothing short of a nightmare. Some of the companies at the forefront of machine learning innovation such as LinkedIn, Uber, Netflix, Airbnb or Lyft have certainly experienced the magnitude of this challenge and they have built specific solutions to address it.

Be honest – how many times have you used the'Recommended for you' section on Amazon? Ever since I found out a few years back that machine learning powers this section – I have been hooked. I keep an eye on that section each time I log into Amazon. There's a reason companies like Netflix, Google, Amazon, Flipkart, etc. spend millions perfecting their recommendation engine. It is a powerful acquisition channel and enhances the customer's experience. Let me use a recent example to showcase their power.

As movie audiences anticipate the return of Arnold Schwarzenegger to his signature role as the original Terminator in November -- yes, he WILL be back -- scientist and rising star in the Artificial Intelligence world Matt Allen has a few thoughts for filmmakers about how AI is depicted in popular culture. His main point is "You're getting it all wrong." "Machine Learning is a popular buzzword, and a powerful tool," said Allen, who has co-authored two articles on AI for the American Chemical Society Nano and Nature Biomedical Engineering, including one on how AI can be used to help detect cancer early in patients who may not even be showing symptoms. "However, no matter how you slice it, machines do not learn. Machine Learning (ML) was named as such in the pursuit of machines that could learn like humans. The furthest we have come in that regard is an optimization procedure wherein at each step during the "training process" a "model" gets slightly better at whatever the task is. This procedure is definitively not "learning". The incremental improvements made by a model may seem intuitively similar to the incremental improvements that humans make when learning to do something new, but the similarities end there."

Lucidea, provider of ArchivEra, CuadraSTAR SKCA and Eloquent Archives, enjoyed a very successful experience at this year's Society of American Archivists (SAA) annual conference. Traffic to their booth was the highest ever, with attendees eager to see how easily their archival collections management solutions enable researchers and the public to connect with the historic materials archivists work hard to preserve. Lucidea's archives specialists demonstrated the powerful and versatile capabilities of ArchivEra, CuadraSTAR SKCA, and Eloquent Archives that make them a valued technology partner in the archives community. Importantly, SAA attendees were the first to see Lucidea's exciting new AI prototype for archives. With Artificial Intelligence (AI) integration now available in ArchivEra, Lucidea's clients will enjoy powerful automatic categorization functionality.

A new deepfake that shows comedian Bill Hader transforming into Hollywood heavyweight Tom Cruise is going viral online. The video, developed by deepfake creator Ctrl Shift Face, is closing in on nearly 3 million views on YouTube alone. The clip centers around Hader's 2008 interview on the Late Show With David Letterman, in which the comedian describes meeting Cruise prior to the filming of action comedy film Tropic Thunder. Hader, who is also known for his incredible celebrity impressions, recalls the meeting while mimicking the Top Gun star. Yet as Hader begins his impression, his face suddenly morphs into Cruise's thanks to software that utilizes artificial intelligence.

In one corner, there's a newbie, 5G-- higher bandwidth networks promising super-fast speed and increased responsiveness and reliability. And in the other, artificial intelligence (AI)--technology that can analyze massive amounts of data and learn from previous actions. But combined together, 5G networks and AI have the potential to pack a significantly more powerful punch. Some industry experts argue that the key differentiator among mobile networks will be the quality of the AI in their systems. "Together, AI and 5G will make possible capabilities that never existed before," says Prakash Sangam, principal and founder of Tantra Analyst, a high-tech research, analysis and strategic advisory firm.

The days of debating if artificial intelligence (AI) will impact the music industry are over. Artificial intelligence is already used in many ways. Now it's time to consider how much it will influence how we create and consume music. Just as it does for other industries, in the music industry, AI automates services, discovers patterns and insights in enormous data sets, and helps create efficiencies. Companies in the music industry need to accept and prepare for how AI can transform business; those that won't will be left behind.