Over the past decade, computer vision has had a tremendous impact. From self-automated cars to space exploration, its various uses in surveillance and image analysis has bettered the world. The in-depth analysis of video footage and images using deep machine learning models has become a powerful tool that most companies and even governments are taking notice of. Recognition and tracking - the computer tries to link any pattern or feature in the image to a previous feature or activity in its database. Prediction - lastly it predicts possible behaviours of the object, and how it will react in the future.

Experts in ethics, privacy and bias in AI spoke at the AI Expo Africa conference held in Cape Town this week. Highlights included an AI art and music challenge, and French company Rhoban who showed off their soccer-playing robots. The robots are also the RoboWorld Cup champions. Mushambi Mutuma, chief executive at tech company Altivex said AI was controlled by the people who input the data. "Last year Google's facial recognition software kept inputting African-Americans as gorillas. We have to have new voices and a bit more diversity because the data will never come back accurate and that's what we've tried to solve from bias," he said.

Humans are now generating an estimated 2.5 quintillion bytes of data every single day, with more data being created in the past two years than in all of human history. Managing this growing flood is complex and the task comes with a high level of responsibility. The 24/7 requirements on business and huge security challenges mean that'manual" management is no longer an option. Particularly when combined together they will let businesses manage and get value from their information more easily, effectively, and with less effort. One technology in particular that is unlocking new levels of value is the autonomous database.

Global Artificial Intelligence (AI) for Telecommunication Industry valued approximately USD 651.2 million in 2017 is anticipated to grow with a healthy growth rate of more than 42% over the forecast period 2019-2025. The Artificial Intelligence (AI) for Telecommunication Industry is continuously growing in the global scenario at significant pace. Artificial intelligence (AI) is group of methodology that focus on formation of intelligent machines with the help of human intelligence such as visual perception, speech recognition, decision-making, and translation between languages. The main application of artificial intelligence in telecommunications is for network management. The two key technologies that are widely in telecommunication industry are expert systems and machine learning.

Communication is key they say, or at least I was told so when I was younger. However, the older I got, the more proof I saw to this age-old saying. We more often than not end up misunderstanding the other that helped create opportunities that allowed ambiguity to prosper. Communication is important, and it gets a lot more important when we're talking business. We're taught back in business 101 that effective and efficient communication is imperative for a business owner if you're looking to run a successful business.

"Customers can't just go buy an'AI'" Today we're pleased to be joined by Darren Roos, the CEO of enterprise software vendor IFS. Enterprise tech buyers are under more and more pressure to innovate faster, grow globally, and deliver healthy margins. Yet when they look to the market for ways through, they are bombarded by vendors slinging their latest offerings associated with some of the biggest tech buzzwords of the day. It's no surprise then that the biggest challenge companies face is navigating this very complex enterprise technology landscape, which is a crowded and noisy space of offerings that promise to fix a myriad of business problems. Many of our customers consider themselves to be in a catch-22: on the one hand, they need to have processes in place to deal with whatever that'future' might entail, but without clear insight into what impact various future scenarios will have on their business, they're unsure how to plan effectively – and which technologies will support this planning.

When it comes to the issue of health in South Africa, particularly when it concerns matters of life and death, artificial intelligence (AI) is seen to offer enormous promise regarding improving outcomes. The technology's ability to gain information, process it and deliver an output, all while recognising patterns in behaviour and creating its own logic, is what distinguishes it. The reality, though, is that AI has already moved beyond the'promise' stage, explains Ronelle Naidoo, head of sales at Mint Group. There are, in fact, already tangible examples of it in use within the healthcare sector. Naidoo points to Dr Raymond Campbell, of Phulukisa Healthcare Solutions, as one example of the type of out-the-box thinking that, when applied to such technology, is beginning to revolutionise the sector.

The performance of land surface models (LSMs) strongly depends on their unknown parameter variables so that it is necessary to optimize them. Here I present a globally applicable and computationally efficient method for parameter optimization and uncertainty assessment of the LSM by combining Markov Chain Monte Carlo (MCMC) with machine learning. First, I performed the long-term ensemble simulation of the LSM, in which each ensemble member has different parameters' variables, and calculated the gap between simulation and observation, or the cost function, for each ensemble member. Second, I developed the statistical machine learning based surrogate model, which is computationally cheap but accurately mimics the relationship between parameters and the cost function, by applying the Gaussian process regression to learn the model simulation. Third, we applied MCMC by repeatedly driving the surrogate model to get the posterior probabilistic distribution of parameters. Using satellite passive microwave brightness temperature observations, both synthetic and real-data experiments were performed to optimize unknown soil and vegetation parameters of the LSM. The primary findings are (1) the proposed method is 50,000 times as fast as the direct application of MCMC to the full LSM; (2) the skill of the LSM to simulate both soil moisture and vegetation dynamics can be improved; (3) I successfully quantify the characteristics of equifinality by obtaining the full non-parametric probabilistic distribution of parameters.

Recognizing the pervasivetalent gapthat exists between data scientists and data workers in the line of business, Assisted Modeling helps teach data science with a guided walk-through and aims to help all data workers, regardless of technical acumen, advance their skill sets in the process of building machine learning models. Our approach in building Assisted Modeling is to advance the skills of the data worker, creating next-level citizen data scientists capable of building the machine learning models required to tackle the advanced analytic challenges of the future. Assisted Modeling provides users the transparency and control needed to build trustworthy machine learning models that drive business outcomes without writing a line of code. As an output of the application, users can access code-free machine learning tools directly within the Alteryx Designer interface. Assisted Modeling allows any data worker to construct machine learning models, understand how and why their models work, and capture modeling decisions, turning raw data into informed business decisions with unprecedented speed and confidence.

Cape Town – Artificial intelligence (AI) is meant to be better and smarter than humans but it too can succumb to bias and a lack of ethics if it isn't fed data to …