When Uncharted: The Lost Legacy was released this year, it gained a lot of attention - not because it is the latest instalment for a popular franchise, it stood out for another reason. The game was set in India, had two lead women, and one of them, Nadine Ross, is a black South African. Other big releases this year include Assassins Creed Origins, which is set in Egypt with an African protagonist, while Star Wars Battlefront II used the likeness and voice of Janina Gavankar, an actress with part-Indian heritage. But speaking to BBC Asian Network, Jo Twist, chief executive for Ukie, the trading body for the UK's games industry, said there was still a long way to go before video games could be truly representative of the gaming audience. She says there are not enough game designers from black or Asian backgrounds working and that this is reflected in what we are playing.

Robots will replace teachers by 2027. That's the bold claim that Anthony Seldon, a British education expert, made at the British Science Festival in September. Seldon may be the first to set such a specific deadline for the automation of education, but he's not the first to note technology's potential to replace human workers. Whether the "robots" take the form of artificially intelligent (AI) software programs or humanoid machines, research suggests that technology is poised to automate a huge proportion of jobs worldwide, disrupting the global economy and leaving millions unemployed. But just which jobs are on the chopping block is still a subject of debate.

Artificial intelligence (AI) is neither friend nor foe – It's both! This was the feedback received following a high level panel discussion held during the Science Forum South Africa (#SFSA2017) in Tshwane, were youth members from various African states exchanged their views and ideals as to how best they, business and government need to approach the digital age. "There is a paralysing fear that jobs will be taken away and humans will become redundant and lazy", said Barbara Glover of NEPAD Planning and Coordinating Agency. Glover added that these fears need to be allayed by understanding how best AI can unlock enhanced potential for humans to deploy their skills in new ways. Ama Duncan of Corporate Training Solutions in Ghana agreed.

To realise data's full potential, Deloitte Data Analytics is demystifying data science through impactful initiatives. In February 2018, the Deloitte School of Analytics will host its three-day annual education event, where they break down this data phenomenon and help clients use data science to drive their organisation forward. The annual education event is a three-day training programme that focuses on enhancing Analytics capabilities across Africa. Deloitte Analytics SMEs and TDWI lecturers will be engaging with the delegates through hands on and classroom based training, structured case study discussions, presentations and practical examples. The education journey will include modules such as Practical Application of Machine Learning and Artificial Intelligence, Data Architecting for the Future and Demystifying Data Science.

Samsung is set to take on Apple, Google and Amazon with a new home speaker using its Bixby AI. The new speaker will go on sale early next year, according to Bloomberg. Samsung has already confirmed it will will'soon' release a smart speaker designed to take on Amazon's Echo, Google's Home and Apple's HomePod. Bloomberg says the speakers will have a'strong focus on audio quality and the management of connected home appliances such as lights and locks.' It will also synchronize with TVs, Galaxy smartphones and other Samsung devices.

As a kid, I saw the 1968 version of Planet of the Apes. As a future primatologist, I was mesmerized. Years later I discovered an anecdote about its filming: At lunchtime, the people playing chimps and those playing gorillas ate in separate groups. It's been said, "There are two kinds of people in the world: those who divide the world into two kinds of people and those who don't." And it can be vastly consequential when people are divided into Us and Them, ingroup and outgroup, "the people" (i.e., our kind) and the Others. The core of Us/Them-ing is emotional and automatic. Humans universally make Us/Them dichotomies along lines of race, ethnicity, gender, language group, religion, age, socioeconomic status, and so on. We do so with remarkable speed and neurobiological efficiency; have complex taxonomies and classifications of ways in which we denigrate Thems; do so with a versatility that ranges from the minutest of microaggression to bloodbaths of savagery; and regularly decide what is inferior about Them based on pure emotion, followed by primitive rationalizations that we mistake for rationality. But crucially, there is room for optimism. Much of that is grounded in something definedly human, which is that we all carry multiple Us/Them divisions in our heads. A Them in one case can be an Us in another, and it can only take an instant for that identity to flip. Thus, there is hope that, with science's help, clannishness and xenophobia can lessen, perhaps even so much so that Hollywood-extra chimps and gorillas can break bread together.

The 4,500-year-old secrets of the Great Pyramid of Giza's maze of hidden chambers are set to be revealed by an inflatable robot. Researchers are building a blimp-like probe that enters ancient monuments via a 3.5-centimetre (1.4-inch) 'keyhole' drilled through a wall. Once inflated inside the chamber, the drone flies like a blimp to explore inaccessible areas with minimal damage to artefacts or structures hidden within. Last month, a mysterious 30-metre (100ft) void nestled above the pyramid's Grand Gallery deep within the monument was discovered by an international team of researchers. The 4,500-year-old secrets of the Great Pyramid of Giza's hidden chambers are set to be revealed by an inflatable robot (artist's impression). Researchers are building a blimp-like probe that enters ancient monuments via a 3.5-centimetre (1.4-inch) 'keyhole' drilled through a wall (left) The device enters rooms and chambers through a 3.5 centimetre hole drilled through a wall by researchers outside. It is made up of two robots, a tubular machine equipped with a high definition camera and a probe that explores the structure via a small, inflatable blimp.

The tech is increasingly becoming ubiquitous across all industries from the automotive industry, fintech, social media, ecommerce to even entertainment. We are living in the age of big data, as increasingly more enterprises invest in AI and machine learning -- a branch of AI which is in its simplest definition is a form of data analysis -- startups are taking notice and disrupting whole industries by employing that tech. With that in mind, here's everything you need to know about the artificial intelligence (AI) and machine learning tech ecosystem in South Africa. There are a large number of South African startups using AI-related technologies in their software solutions. Here below is a list of some of the more well-known startups -- some of which have developed cutting AI solutions, or potentially disruptive technologies using AI.

I had the pleasure of meeting Shing Tao, the CEO of Remark Holdings (NASDAQ:MARK), on Monday night. He had just flown into San Francisco from Beijing and I was meant to be his first meeting before dinner and some shut eye; he had a big Tuesday in front of him with Roth Capital (whose analyst loves MARK, although they haven't been their bankers) taking him on a Non-Deal Roadshow and introducing him to several institutional investors. Remark has been very hot lately, jumping almost 200% in value since the company reported Q3 earnings in mid-November. The move came on the back of 2018 revenue guidance for their Artificial Intelligence operating subsidiary, KanKan. They guided to $30M in KanKan AI revenues for next year, which would be a 500% increase over 2017.

Despite the recent successes in the use of machine learning (ML) to perform tasks more accurately than humans such as in cancer detection and hand-writing recognition, moving beyond a demo to roll out a live commercial product requires much more than a fancy algorithm. There's no doubt that the technology will re-define most industries, but it's worth keeping in mind that we are just at the beginning of a multi-decade cycle and so entrepreneurs should be cognisant of this when implementing ML in a commercial environment with clients. What follows are some lessons learnt from being in the trenches. It's worth clarifying that most problems in ML follow a similar pattern, loosely called "predictive analytics": Building these models requires huge datasets of labeled historical records. For example: loan applications with tags stating whether a loan repayment event occurred in order to make a prediction whether an applicant will repay a loan. The dataset requirement may not sound problematic (even assuming the requisite datasets are available), but the reality is that these datasets are often residing in different areas of the business, in different formats with different labels and with different decision-makers for each dataset.