Two weeks ago, I compiled a list of the 5 jobs robots will take first. Last week, I compiled a list of the 5 jobs robots will take last. Both previous essays are about robots replacing human workers who do cognitive nonrepetitive work (such as middle managers, salespersons, tax accountants, and report writers) that most people do not believe robots will be able to do any time soon. For those essays, I defined robots as technologies, such as machine learning algorithms running on purpose-built computer platforms, that have been trained to perform tasks that currently require humans to perform. For this writing, let's expand the definition of robot to any autonomous system designed to do work that used to require humans to perform.

The tech revolution is coming to advertising. Chatbots are replacing humans, big data threatens our privacy, and the blockchain is linking it all together. In our series on tech and advertising, we're taking a look at how the industry is being reshaped. Soon, advanced computers won't just be driving you to work, they'll be selling you stuff as well. We can already see this in the form of chatbots.

Unisys Corporation has announced new advanced data analytics milestones including the launch of its new Machine Learning-as-a-Service offering and the proposed launch of its new Artificial Intelligence Center of Excellence. The announcements were made at the Strata Hadoop World conference which is being held in San Jose, California. "Analytics are rapidly emerging as a critical tool for digital business transformation, because they enable organizations to discover the value they can gain from marshalling often-disparate business assets in novel ways," says Dr. Rod Fontecilla, vice president and global lead, Analytics, Unisys. This new service offering will be a part of the Unisys Analytics Platform. It consists of a library of machine algorithms which can be used along with proven processes and methodologies to analyze data and facilitate predictive analytics.

When applied to the S&P 500 constituents from 1992 to 2015, their stock selections generated annual returns in the double digits -- whereas the highest profits were made at times of financial turmoil. In March 2016, South Korean Lee Sedol, one of the best Go players in the world, lost to the AlphaGo computer program. It was a milestone in the history of artificial intelligence because up to that point the Asian board game had been considered too complex for computers. Behind successes such as this are programs that are modelled on biological systems and are constructed in a form similar to neural networks so that they can independently extract relationships from millions of data points. 'Artificial neural networks are primarily applied to problems, where solutions cannot be formulated with explicit rules,' explains Dr. Christopher Krauss of the Chair for Statistics and Econometrics at FAU. 'Image and speech recognition are typical fields of application, such as Apple's Siri.

It's clear that artificial intelligence (AI) is already one of the defining trends in fintech in 2017 and an increasingly popular buzz word in the industry. Businesses are gradually understanding the importance and benefits of machine-learning technology. Self-made billionaire Mark Cuban has boldly claimed that the "the world's first trillionaire will be an AI entrepreneur." He goes on to say that faster computer processers and large data sets have the ability to push AI into a wealth of industries and services. We have access to more data today than ever before, and with the increase of solution-finding apps that help consumers find patterns in their habits, businesses and start-ups, the bars are now high for the fintech industry.

The next assistant in many offices could be named Alexa or Cortana. In 2016, Silicon Valley obsessed over how text-based bots in apps like Slack could make employees more efficient, turning complicated tasks or forms into conversational texts. Now, following the success of Amazon Inc.'s Alexa and Alphabet Inc.'s Google Home, people in the technology industry are increasingly thinking about how such voice-activated devices can be made useful in the workplace. The workplace offers challenges that experts say intelligent assistants built for home use so far haven't effectively met, mostly in the area of voice recognition. Workers at Goodwinds Inc. in New York City, for example, have used an Amazon Echo attached to the office ceiling for such tasks as adding events to their calendars and setting reminders for meetings, says Vinay Patankar, chief executive of the workflow-management startup.

That's because many online-only banks and others are investing in automated assistants that improve customer interactions by predicting why customers are contacting the bank and resolving the issue quickly, according to Servion Global Solutions. "Our philosophy is that a customer has a reason for making contact with his bank and over a period of time, as the interaction history builds up, the bank should be able to predict why the customer is calling, regardless of the channel being used," says Ashish Koul, Servion senior vice president and general manager. "Instead of letting the customer go into [interactive voice response] mumbo jumbo of pressing 1 for this and pressing 2 for that … you have a virtual assistant, which is intelligent, powered by analytics and can either solve the problem or connect the customer to the most qualified human when that interaction requires it." Servion predicts AI will support 95 percent of customer interactions with banks by 2025. The company's ServIntuit platform provides the context of previous customer interactions across channels to make the virtual assistant more intelligent, the company says.

IBM has spent years developing its AI platform, Watson, looking to the open-source Apache Spark platform for innovative machine learning capabilities that can help push Watson into business verticals for manufacturing, healthcare, cybersecurity and retail. Having made the IBM Spark Technology Center a part of its AI-driven ecosystem, IBM has opened up the analytics capabilities of Watson in an effort to simplify the machine learning for progressive neural networking using Apache SystemML. The idea is to help businesses move quickly into the new domains of machine learning.

This playlist contains the lecture videos for the Deep Natural Language Processing course offered in Hilary Term 2017 at the University of Oxford. This is an advanced course on natural language processing. Automatically processing natural language inputs and producing language outputs is a key component of Artificial General Intelligence. The ambiguities and noise inherent in human communication render traditional symbolic AI techniques ineffective for representing and analysing language data. Recently statistical techniques based on neural networks have achieved a number of remarkable successes in natural language processing leading to a great deal of commercial and academic interest in the field This is an applied course focusing on recent advances in analysing and generating speech and text using recurrent neural networks.

Global leader in digitally-powered business process management and services Genpact has launched its Artificial Intelligence (AI) Reporting solution that harnesses the power of AI technologies to automate financial planning and analysis (FP&A) operations and drive more timely, insightful reporting. CFOs have fast, seamless access to both internal and external data sources like never before, driving more accurate forecasts for quicker, smarter business decisions. Based on multiple studies including a study done jointly by the Genpact Research Institute and HfS Research, CFOs across the board struggle with three challenges in providing actionable insights to their business leaders, including the ability to get relevant disparate data from internal and external sources; delay and the effort it takes for their teams to manage and do basic analysis on that data, reducing its relevance and leaving little scope for predictive analytics and business insight value adds; and lack of reporting mechanism that creates the user experience for business leaders to use that data to drive real time actions. Genpact's cloud-based AI Reporting solution uses advanced digital technologies to help enterprises reimagine the end-to-end financial reporting process. By integrating structured and unstructured data from internal and external sources, and automating reporting processes with predictive analytics, natural language processing and generation, and machine learning, the solution drives greater agility to adapt to new business requirements.