What do you think of artificial intelligence when it comes to your mind? All of us probably think of robots walking around us, resembling us and taking care of our desires. Are you feeling vaguely uneasy with this approach? It is estimated in a survey held in 2021 that 10.9 billion dollars will be spent on intelligent process automation (IPA). Doesn't it sound too interesting to you? Everything will be covered in this article.

We present a chatbot implementing a novel dialogue management approach based on logical inference. Instead of framing conversation a sequence of response generation tasks, we model conversation as a collaborative inference process in which speakers share information to synthesize new knowledge in real time. Our chatbot pipeline accomplishes this modelling in three broad stages. The first stage translates user utterances into a symbolic predicate representation. The second stage then uses this structured representation in conjunction with a larger knowledge base to synthesize new predicates using efficient graph matching. In the third and final stage, our bot selects a small subset of predicates and translates them into an English response. This approach lends itself to understanding latent semantics of user inputs, flexible initiative taking, and responses that are novel and coherent with the dialogue context.

We recently took a deep dive into how AdSkate makes digital advertising smarter with AI contextual targeting. Now we sit down with the founders to learn more about their stories. Tell me about the moment that sparked the idea for your product? Shreyas and Salil were exploring different applications for computer vision and machine learning models and they discussed the power of these technologies with Akaash. Akaash who was working at a large ad agency at the time suggested that the digital ad space right now is facing a lot of problems around privacy and brand safety aspects and this tech might just be the solution.

The task of image captioning aims to generate captions directly from images via the automatically learned cross-modal generator. To build a well-performing generator, existing approaches usually need a large number of described images, which requires a huge effects on manual labeling. However, in real-world applications, a more general scenario is that we only have limited amount of described images and a large number of undescribed images. Therefore, a resulting challenge is how to effectively combine the undescribed images into the learning of cross-modal generator. To solve this problem, we propose a novel image captioning method by exploiting the Cross-modal Prediction and Relation Consistency (CPRC), which aims to utilize the raw image input to constrain the generated sentence in the commonly semantic space. In detail, considering that the heterogeneous gap between modalities always leads to the supervision difficulty of using the global embedding directly, CPRC turns to transform both the raw image and corresponding generated sentence into the shared semantic space, and measure the generated sentence from two aspects: 1) Prediction consistency. CPRC utilizes the prediction of raw image as soft label to distill useful supervision for the generated sentence, rather than employing the traditional pseudo labeling; 2) Relation consistency. CPRC develops a novel relation consistency between augmented images and corresponding generated sentences to retain the important relational knowledge. In result, CPRC supervises the generated sentence from both the informativeness and representativeness perspectives, and can reasonably use the undescribed images to learn a more effective generator under the semi-supervised scenario.

As exciting as all this might seem, this decision seems to be more of an aberration than the rule. Before it was finally granted a patent in South Africa, the DABUS application had been rejected by patent offices in the US, Europe and the UK. The European Patent Office (EPO), justifying its decision to reject the patent application, pointed out that the law designates a natural person as the inventor of a work in order to preserve her moral right over the invention as well as to secure for her the economic rights made available by the patent. In order to be entitled to these benefits, an inventor needs to have actually "performed the creative act of invention". While artificial intelligence algorithms today are capable of perform complex computational functions that are often way beyond the capability of humans, the EPO pointed out that in all these instances, the programs are doing little more than just following the broad instructions of the humans who designed them.

As scientists laid the foundations of artificial intelligence, Pamela McCorduck was there. McCorduck, an author who wrote some of the first novels and histories about AI and was a generous friend of CMU, died Oct. 18. McCorduck described herself as an eyewitness to the birth and growth of AI. She was possibly best known for her 1979 book, "Machines Who Think," which chronicles the history of AI from the dreams and nightmares of ancient poets and prophets to the scientific discoveries of the 20th century. The novel contains the famous quote, "Artificial intelligence began with the ancient wish to forge the gods."

At a recent conference on the challenges of artificial intelligence, Christof Koch made clear in his remarks that the stakes were high: "By mid-century, humanity will be surrounded by ubiquitous, flexible, highly intelligent autonomous agents, and this will profoundly affect our future--including whether we have any." Dr. Koch--who is the chief scientist of the Mindscope Program at the Allen Institute for brain science in Seattle--was speaking to a group of roughly a hundred academics, diplomats and journalists. The conference was hosted by the Vatican at the Cancelleria, a 15th-century Renaissance palace in Rome, and centered around the theme of "the challenge of artificial intelligence for human society and the idea of the human person." This was the second event at the Vatican to focus on artificial intelligence, commonly abbreviated as A.I. Just before Italy entered into a nationwide lockdown last year, the Pontifical Academy for Life held a workshop on A.I. in February 2020. This workshop ultimately produced a "Call for AI Ethics," which was signed by Microsoft, IBM, the Food and Agricultural Organization of the United Nations and the Italian government, in addition to the Academy.

I don't know much about whales. I have never seen a whale in my life,

In the coming years, mobility solutions--or how we get from point A to point B--will bridge the gap between ground and air transportation--yes, that means flying cars. Technological advancements are transforming mobility for people and, leading to unprecedented change. Nand Kochhar, vice president of automotive and transportation for Siemens Software says this transformation extends beyond transportation to society in general. "The future of mobility is going to be multimodal to meet consumer demands, to offer a holistic experience in a frictionless way, which offers comfort, convenience, and safety to the end consumer." Thinking about transportation differently is part of a bigger trend, Kochhar notes: "Look at few other trends like sustainability and emissions, which are not just a challenge for the automotive industry but to society as a whole." The advances in technology will have benefits beyond shipping and commute improvements--these technological advancements, Kochhar argues, are poised to drive an infrastructure paradigm shift that will bring newfound autonomy to those who, today, aren't able to get around by themselves. Kochhar explains, "Just imagine people in our own families who are in that stage where they're not able to drive today. Now, you're able to provide them freedom." Laurel Ruma: From Technology Review, I'm Laurel Ruma, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace. Our topic today is the future of mobility. In 2011, Marc Andreessen famously said, "Software is eating the world."

Hearing "No" at a job interview can be quite a demotivating experience. Applying for a data science job involves a lot of stages, while the interview stage can be crucial to gauge if an applicant will fit the job role and the company culture. Often, applicants get rejected for mistakes they commit that can easily be avoided. There is a huge hype around using words like deep learning, machine learning, and neural networks in a CV while applying for data science roles. An applicant should mention such skills in their CVs or during the interview only if one has worked on real-life projects previously in a job or internship.