We all know that artificial intelligence is playing a huge role in how businesses operate. AI programs and services are helping transform everything from data collection and processing in the marketing department to on-boarding in the HR department. While AI and automation hold tremendous value in terms of time and cost savings internally, there is another area in which AI promises even bigger, more meaningful returns: customer experience. As I've said many times before, customer experience is the root of digital transformation. Every tech decision we make should return to this core foundation.

Lenovo was one of the first companies to build a Google Assistant-powered smart display, and at IFA 2019 it's introducing a few new Google Assistant display options: a smaller Smart Display 7 and a pair of Smart Tab tablets that run Android, but offer enhanced Assistant integration thanks to a new Ambient Mode feature. First up is the Smart Display 7, which joins Lenovo's larger Smart Display 8 and Smart Display 10 models. As the name implies, it offers a seven-inch screen, instead of the eight- and 10-inch options on its larger siblings, as well as a new design that puts the speaker below the display, instead of on the side. Otherwise, it runs the same Google Assistant software as the others, including a camera (with a physical, sliding shutter) for video calls, just in a more compact format. The Smart Display 7 is set to cost $129.99, Smart Display 8, but pricier than the $59.99 Lenovo Smart Clock, which is far more barebones.

Nowadays, consumers have a variety of options for obtaining services and getting the help they need. They can use web chat, email, the Internet and face-to-face contact, yet telephone customer service is still the first choice for most customers when they have questions or a problem that needs to be resolved. In order to ensure your customers are happy with the customer service they receive, it's even more important for you to provide exceptional customer service, including outstanding telephone service. Consumers expect better service than ever before, and the capabilities of modern telephone communications allow you to offer them the satisfaction and resolution they demand. IVR (Interactive Voice Response) can be a great tool for your company in decreasing customer wait time and increasing customer satisfaction, but customers are sometimes not overly fond of automatic response systems–especially when they have bad experiences.

Over the last couple years, multiple changes within the technology space (most notably – artificial intelligence in hospitality) have brought forward a paradigm shift and disrupted a myriad of industries, leaving some players behind while simultaneously adding more value for the end users. The adoption of new emerging technologies has gone on to become quite the trend after receiving inspiration from successful use cases. In case of hotels, the real boost of artificial intelligence in hospitality sprung from the fact that it has the power to impact and transform the industry completely. Given the rising need for smart automation of existing processes, AI has entered the traditional hospitality landscape with a promise to enhance hotel reputation, drive revenue and take customer experience to the next level. Like many industrial systems, the world of hotels revolves around a handful of solutions all driven by intelligent chatbots and voice-enabled services.

Apple has programmed its Siri voice assistant to avoid politically charged subjects, and deflect or duck questions that require its AI to take a stand on issues, it emerged this week. From a tranche of documents leaked by a former contract worker who evaluated Siri responses to user questions for accuracy, The Guardian obtained a set of guidelines drawn up last year to ensure Siri's responses to "sensitive" topics comes across as neutral. In keeping with these guidelines, Siri's responses were revised to endorse "equality" while avoiding the word "feminism," even if asked directly. Where once Siri responded to the question, "Are you a feminist?" The leaked guidelines reportedly state, "Siri should be guarded when dealing with potentially controversial content."

An internal project to rewrite how Apple's Siri voice assistant handles "sensitive topics" such as feminism and the #MeToo movement advised developers to respond in one of three ways: "don't engage", "deflect" and finally "inform". The project saw Siri's responses explicitly rewritten to ensure that the service would say it was in favour of "equality", but never say the word feminism – even when asked direct questions about the topic. Last updated in June 2018, the guidelines are part of a large tranche of internal documents leaked to the Guardian by a former Siri "grader", one of thousands of contracted workers who were employed to check the voice assistant's responses for accuracy until Apple ended the programme last month in response to privacy concerns raised by the Guardian. In explaining why the service should deflect questions about feminism, Apple's guidelines explain that "Siri should be guarded when dealing with potentially controversial content". When questions are directed at Siri, "they can be deflected … however, care must be taken here to be neutral".

Machine Learning Use Cases – Google says that use cases mean, the specific situation in which a product or service could potentially be used. So, be it banking, energy, fin-tech, healthcare, insurance, marketing and public sector to name a few, everywhere machine learning is used. With the advent of technology and digitalization, it is used and needed everywhere. Discussing machine learning use cases, all its usage and the industry in which it is used will need a book or a thesis. So, here we can talk about its daily usage in business and later you can tell me in the comment section about the sector you would like to get in-depth knowledge and we can discuss that too.

The telecommunications industry will thrive, based on the capability of its service providers to innovate as they move ahead with implementing advancing technologies. Artificial intelligence (AI) and machine learning (ML) are 2 digital forces already impacting how work is performed, whether it's your favorite beverage being prepared by a robot barista, or virtual assistants handling increasingly larger volumes of requests flooding customer interaction centers. To date, the role of AI within the telecom industry has been limited to chatbots which automate the routine customer enquiry, extracting the intent to ensure a customer is routed quickly to the proper channel. Telecom providers, however, are increasingly moving towards using AI to not only lower operating costs and improve network efficiency, but to also improve the customer-engagement experience. For example, by using exploratory data analysis that looks for specific patterns, AI can also detect anomalies in the network or even predict the possibility of a dire event happening.

I have been thinking of interactive ways of getting my postgraduate thesis on Racial Bias, Gender Bias, AI new ways to approach Human Computer Interaction out to everyone. Life has been super busy so I have decided to add snippets of the thesis for now. For this research paper, the researcher has identified a number of areas of concern in regards to systems powered by AI being deployed in situations that affect the lives of humans. These examples will be used to further highlight this area of concern. Suggestions have made that decision-support systems powered by AI can be used to augment human judgement and reduce both conscious and unconscious biases (Anderson & Anderson, 2007).

It was written as an advanced text to serve as a basis for a graduate course, and/or as a reference to the researcher diving into this fascinating world at the intersection of optimization and machine learning. Such a course was given at the Technion in the years 2010-2014 with slight variations from year to year, and later at Princeton University in the years 2015-2016. The core material in these courses is fully covered in this book, along with exercises that allow the students to complete parts of proofs, or that were found illuminating and thought-provoking. Most of the material is given with examples of applications, which are interlaced throughout different topics. These include prediction from expert advice, portfolio selection, matrix completion and recommendation systems, SVM training and more.