The adoption of robotics and artificial intelligence (AI) in Africa received a major boost after Uniccon Group, an Abuja-based tech startup, unveiled the continent's first humanoid robot. Omeife, the 1.8m female human-like robot, is African by design and has Igbo-like physical attributes. The battery-powered robot can speak Igbo, Yoruba, English, French, Swahili, Wazobia, Pidgin, Afrikaans and Arabic with native accents. Uniccon Group chief executive Chuks Ekwueme said: "Omeife also identifies objects and calculates positions and distances of objects." The launch of Omeife comes a few months after Abdul Malik Tejan-Sie, a South African-based Sierra Leonean innovator, presented a prototype of South Africa's first humanoid robot.

Kissterra's proprietary software is designed to help insurers target their marketing effort toward their key audience. The company has focused early on the US auto insurance market. "We felt like we can bring initially the most value in the auto insurance vertical," Kerzner said. Customers include auto insurance carriers, insurance providers and large agencies. While he declined to name names, Kerzner said that Kissterra has conducted business with three of the top five auto insurance carriers in the US, as well as some direct-to-consumer players.

There's a huge opportunity to position India as a global hub for data and artificial intelligence (AI), enabling investment, jobs and innovation, says Anant Maheshwari, president, Microsoft India. How prepared are enterprises today in a pandemic-stricken marketplace? Across every industry and sector, we've seen years' worth of digital transformation happen over the last few months. Organisations, both in private and public sector, are quickly adapting to new ways of working and serving customers. Innovation is being applied as organisations work on transforming products, services and business models to stay relevant.

The AI and ML deployments are well underway, but for CXOs the biggest issue will be managing these initiatives, and figuring out where the data science team fits in and what algorithms to buy versus build. Pure Storage launched a bevy of artificial intelligence tools, including the AI Data Hub, which are designed to meld storage and AI workflows from design to deployment. The storage company said it co-developed the AI Data Hub with Nvidia to break down the data silos that hamper analytics and model development. Storage vendors have been increasingly focused on AI workloads and managing data workflows instead of just storing it. Pure has been using its AI-Ready Infrastructure (AIRI) and expertise in solid-state storage via its Flashblade infrastructure to grab next-gen workloads.

When comes to machine learning, data is certainly the new oil. The processes for managing the lifecycle of datasets are some of the most challenging elements of large scale machine learning solutions. Data ingestion, indexing, search, annotation, discovery are some of the aspects required to maintain high quality datasets. The complexity of these challenges increase linearly with the size and number of the target datasets. While it is relatively easy to manage training datasets for a single machine learning model, scaling that process across thousands of dataset and hundreds of models can become nothing short of a nightmare. Some of the companies at the forefront of machine learning innovation such as LinkedIn, Uber, Netflix, Airbnb or Lyft have certainly experienced the magnitude of this challenge and they have built specific solutions to address it.

Amundsen combines it backend architecture with a simple user experience that enables the search and exploration of datasets. Netflix has been an active contributor to open source technologies in the big data space and data discovery and management is not the exception. Metacat is Netflix's solution to automate the lifecycle of metadata assets. Functionally, Metacat is a federated service providing a unified REST/Thrift interface to access metadata of various data stores.

Human trafficking is a crime that takes place largely in the shadows. Victims, who are mostly women and children, often lack legal documentation in the country where they are forced to work or perform sex acts, and many fear reprisals if they go to authorities. Perpetrators, for obvious reasons, take great pains to conceal their behavior by laundering money and keeping their operations quiet. And others who engage in trafficking-related criminal activity -- such as individuals looking to connect with trafficked sex workers -- also have powerful incentives to hide their participation. Recently, law enforcement agencies and organizations that help victims of human trafficking have begun using artificial intelligence tools to overcome this lack of visibility.

Federal IT managers, focused on modernizing government in a climate where the long-standing mantra is "Do more with less," are increasingly optimistic about the potential of artificial intelligence to transform the IT landscape. As with any technology, success is not a given. Instead, AI requires a strong foundation -- a data-centric architecture that optimizes compute power, storage and data to create both a powerful innovation incubator and a transformation accelerator. IT managers within the federal government see the potential of AI. According to a recent study, 77 percent say this technology will change the way government thinks about and processes information, while 61 percent say the technology could solve one or more of the challenges their agency face today.

Editor's Note: This piece was written by Casey Talon, a principal research analyst contributing to Navigant Research's Building Innovations program. The opinions represented in this piece are independent of Smart Cities Dive's views. Artificial intelligence (AI) is technology that learns. This concept encompasses machines that take in continuous data and adapt for perpetual evolution from the objective they were designed to achieve. Today, AI has captured mindshare and headlines, claiming market disruption across industries ranging from trucking to shopping.

James Slifierz is making the rounds of New York City investors as he prepares to move his startup -- Skywatch -- into the Communitech Data Hub in Waterloo. The facility at Erb and Albert streets, slated to open in mid-May, is Communitech's latest tech accelerator. It is for startups that are working with big data and its many applications in artificial intelligence, machine learning and the Internet of Things. "I think it is an exciting opportunity," Slifierz says. He believes the Data Hub will be ideal for Skywatch, which is building an automated program that provides easy access to the enormous amounts of data collected by satellites. The technology is in private testing right now.