The relationship between humans and machines has captivated people for years--from robots that can win Jeopardy to those that can sense human emotions, the list is endless. Today, however, machines are no longer simply a novelty. The rise of analytics and artificial intelligence (AI) has become a key component of a business' success. In the banking world, there are many examples to point to: self-service tellers, automated loan processing, payments, etc. But there's one in particular that is fascinating--the robo-advisor.

Worries that robots may end up replacing human workers have lately sparked debates over the role of artificial intelligence in corporate jobs. During a Fortune conference that tackled the subject and its various implications, voices from IBM and Google proceeded to dissipate concerns and state there's no need for alarm. Present at the Most Powerful Women International Summit in Hong Kong on March 2, women leaders of the two tech companies reassured the public that there's no need to worry about an A.I. takeover, as the technology is here to help and could significantly improve the activity of firms. According to Vanitha Narayanan, chairman of IBM India, the first corporate sector to gain the most from this technology would be that of service-oriented companies. Just like what her boss, IBM CEO Ginni Rometty, previously explained at the World Economic Forum, Narayanan reaffirmed the idea of a partnership between humans and machine.

I listened once to an old professor talking about working out factorial ANOVA and multiple regression on paper, back in the day. He described a whole room full of papers, all in a particular order, every sum contributing to the next sum and if you got one number wrong the whole thing would be wrong and you'd have to start over. Adding machines made it go quicker, and when punch-card mainframes came they were, well, frustrating at times -- lots of graphic and emotional stories there! By the end of the nineties everybody had a computer on their desktop, mostly with a connection to the brand-new Internet. SAS, SPSS, and other statistical and data-management packages became crucial office workhorses, and don't forget Excel.

The nagging frustrations of ill-fitting shoes could soon be a thing of the past thanks to technology developed by Swedish company Volumental. New Balance has partnered with Volumental to install 3-D foot scanners in select stores worldwide that use foot measurements such as width, height and girth to determine a customer's ideal shoe model and shoe size. Although the technology behind Volumental's scanners may be complex, the process is simple: Customers step into the 3-D foot scanner, which transfers information from their feet to a tablet within seconds. The data is then matched with the proper shoes and size for a perfect fit. "Volumental's product has created a completely new way of interacting with our customers. It brings New Balance's unique fit story to life in a personal & relevant way for each of them. Customers love it and our staff love it," said New Balance international retail director Jonathan Clark.

In the past year, I've become convinced that machine learning is not hype. Strong AI/AGI is no longer a requirement for complex tasks. It doesn't matter that AGI is out of reach, since we don't need it in order for automation to take over vast swathes of the job market. I now think that domain specific ML is going to take 10% to 50% of all jobs in the next few decades. The obvious ones are transportation and logistics.

The leader of one of the world's largest automobile producers expects that cars will soon drive themselves and sync to the world around them - but don't count out the human behind the wheel just yet. Carlos Ghosn, the chief executive and chairman of an alliance that includes Nissan, Renault and Mitsubishi, said Thursday that humans will remain involved in the operation of vehicles for the foreseeable future, even as cars with self-driving technology enter the market in the next five years. You will push a button to activate the car's autonomous driving feature, he said, but it will encounter everyday scenarios it cannot compute and that require human assistance. "Artificial intelligence is still way below the creativity of the human brain," Ghosn said. Imagine a self-driving car coming upon a broken-down vehicle in the road, but there is a solid line to either side of it, Ghosn said.

Artificial intelligence has made some great developments toward speeding up cancer diagnosis so far in 2017. Last month it was announced that AI from Sophia Genetics was helping to accelerate patient diagnosis across Latin America. Earlier this year researchers at Stanford University developed a deep learning algorithm that can analyse skin cancer as accurately as a human doctor. Now, Israel-based company, Medical EarlySign has announced the ability of its AI tool to identify the top 1% at highest risk of undiagnosed colorectal cancer (CRC). The machine learning developer announced the first-year results of its implementation with Maccabi Healthcare Services (MHS), for ColonFlag, a tool developed in collaboration with MHS to identify individuals with a high probability of having CRC.

Dr. Stephen Parker, VP of professional graphics, took the stage to give an overview of the use of artificial intelligence in graphics applications. While the first working algorithm using deep feedforward perceptrons was published around 52 years ago in 1965 by Alexey Ivakhnenko and Valentin Lapa, deep learning in graphics applications has reached a combinatorial explosion thanks to some great work that has been recently accomplished by a group of researchers at the University of Toronto. In 2012, professor Geoffrey E Hinton and two students, Alex Krizhevsky and Ilya Sutskever, entered an image recognition content to build computer vision algorithms that learned to identify millions of objects in millions of pictures. Using the most efficient algorithms at the time, the team was able to take the error rate of an average human and cut it in half. They later created a company called DNNresearch, which Google bought the following year.

Top professional poker players have been been beaten by AI bots at no-limits hold'em. A complex variant of poker is the latest game to be mastered by artificial intelligence (AI). And it has been conquered not once, but twice, by two rival bots developed by separate research teams. Each algorithm -- which plays a'no limits' two-player version of Texas hold'em -- has in recent months hit a crucial AI milestone: they have beaten human professional players. The game first fell in December to DeepStack, developed by computer scientists at the University of Alberta in Edmonton, Canada, with collaborators from Charles University and the Czech Technical University in Prague. A month later, Libratus, developed by a team at Carnegie Mellon University (CMU) in Pittsburgh, Pennsylvania, achieved the feat.