Domain Mondo's weekly review of technology news: Feature • Tech Story of the Year: Tay, Microsoft's Artificial Intelligence (AI) Chatterbot: "As many of you know by now, on Wednesday [March 23, 2016] we launched a chatbot called Tay. We are deeply sorry for the unintended offensive and hurtful tweets from Tay, which do not represent who we are or what we stand for, nor how we designed Tay. Tay is now offline and we'll look to bring Tay back only when we are confident we can better anticipate malicious intent that conflicts with our principles and values ... The logical place for us to engage with a massive group of users was Twitter. Unfortunately, in the first 24 hours of coming online, a coordinated attack by a subset of people exploited a vulnerability in Tay. Although we had prepared for many types of abuses of the system, we had made a critical oversight for this specific attack. We take full responsibility for not seeing this possibility ahead of time. We will take this lesson forward as well as those from our experiences in China, Japan and the U.S. Right now, we are hard at work addressing the specific vulnerability that was exposed by the attack on Tay."--Learning from Tay's introduction blogs.microsoft.com

How long does it usually take you to pick out a new pair of glasses at the store? 10 minutes? When left unsupervised, I've admittedly taken over an hour. It's a big deal, as it is scientifically established that the type of glasses you wear impacts perception of your intelligence, success, and attractiveness. It's 2016; there must certainly be some sort of technology that has solved this problem. Of course there is! DITTO technologies developed a virtual mirror that allows customers to try on hundreds of products from the comfort of their homes.

Deep Learning compared to other Machine Learning methods is remarkably modular. This modularity gives it unprecedented capabilities that places Deep Learning head and shoulders above any other conventional Machine Learning approach. Recent research however is pointing to even greater modularity than previously. It is likely that quite soon, monolithic Deep Learning systems will become a thing of the past. Before I discuss what is coming in the future, let me first discuss the concept of modularity.

Janitorial services vendors and workers, through the years, have been plagued by inefficient and labor intensive processes that often result in an insufficient level of cleanliness and sanitization, a challenge that is currently the focus of several artificial intelligence developers and robotic cleaning tool manufacturers. Everyone, to some degree, is familiar with the Roomba robot vacuum cleaner line, manufactured by iRobot. Love them or hate them, the robots represent the steady march of humanity toward the automation of time-consuming tasks that often lead to repetitive motion injuries in operators. However, the manner in which the systems go about their duties, as well as the reasoning behind automated decisions, is not without its critics. A commonly cited issue with current technologies is the lack of transparency and understanding regarding the underlying instructions, performance, and functionality of the systems.

I want to create music generated by mathematical algorithms, or even turning big data files into sound files, just like NASA turned electromagnetic signals from space into music. Producing artificially generated music is a popular subject, see for instance Composing Music With Recurrent Neural Networks, or Using Machine Learning to Generate Music. My question is how to access my laptop's speaker from a script written in Python or Perl. I used to do it long ago in C language, using the command sound available in the Borland package. Today I tried various system calls from within Perl, or directly from the command line, to non avail.

Opinions expressed by Forbes Contributors are their own. The author is a Forbes contributor. The opinions expressed are those of the writer. We are witnessing a historic moment for technology advancement. Today we can pull together the best hardware, affordable infrastructure and vast amounts of data to fundamentally transform the way we conduct business.

"Alexa, how are you different than Siri?" "I'm more of a home-body" I'm away from my desk, so I guess I can't ask Alexa. No problem, I've got an iPhone in my pocket. "Hey Siri, what's the status of my Amazon order?" "I wish I could, but Amazon hasn't set that up with me yet." IPAs (intelligent personal assistants*) are in their infancy, but they are a next major step in human-computer interaction. With the expected concurrent growth of IoT and connected devices, IPAs will be everywhere soon.

Microsoft Ventures today announced two steps that point to how the tech giant's VC arm wants to get involved in artificial intelligence in a big way. First, it's now going to pursue investments through a special fund dedicated to AI startups that focus on "inclusive growth and positive impact on society." Second, it is the first announced backer for Element AI, a new incubator out of Montreal co-founded by "the godfather of machine learning" Yoshua Bengio, which is dedicated to the space. As with its news in May first announcing Microsoft Ventures and its initial focus on cloud-based startups, the VC firm is not specifying just how much money it intends to invest in artificial intelligence, or in Element AI specifically. Element AI, in which Microsoft is taking a seed investment, is not the only AI investment that Microsoft Ventures is making public today. The broader area of AI feels like the technology of the moment, with its many branches used across any and every service, platform and feature to -- simply put -- make tech more efficient, smarter -- and possibly more frightening.

In recent years, AI (artificial intelligence) researchers have finally cracked problems that they've worked on for decades, from Go to human-level speech recognition. A key piece was the ability to gather and learn on mountains of data, which pulled error rates past the success line. In short, big data has transformed AI, to an almost unreasonable level. Blockchain technology could transform AI too, in its own particular ways. Some applications of blockchains to AI are mundane, like audit trails on AI models. Some appear almost unreasonable, like AI that can own itself -- AI DAOs. All of them are opportunities. This article will explore these applications. Before we discuss applications, let's first review what's different about blockchains compared to traditional big-data distributed databases like MongoDB. We can think of blockchains as "blue ocean"databases: they escape the "bloody red ocean" of sharks competing in an existing market, opting instead to be in a blue ocean of uncontested market space.

CRISPR gene editing technology became nearly a household name with its potential to affect humanity. And a baby was born with three parents. While some decry the developed world is falling apart due to changing political environments, science and technology innovation is likely to continue thriving. In fact, innovation is occurring so fast, I believe 2017 will be the year governments begin to consider forming new science, technology, and futurist agencies and organizations to better contend with the rapid change. The old ones are mired in bureaucracy, conservative religious ideology, and the past--unable to contend with issues like nanotechnology, artificial intelligence, and virtual reality.