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New report outlines SA’s biggest challenges to AI adoption

7th December 2023 No Comments

Take yourself back to February 2020. Life was relatively normal, kids were at school, we physically went into work, and everyone was more certain of the paths they were on. A year later, people of all ages are now a lot more tech savvy, having been forced to work-from-home, do online schooling or have online gatherings, just to keep in touch with loved ones. We have had to embrace the change, and step out of our comfort zones, learning how to use technology to navigate everyday life. While it’s true that South Africa is still behind in digitization, it’s catching up fast thanks to COVID-19, catalyzed by boardrooms across the country focusing on digitization like never before. One such focus is the efficiency driven by Artificial Intelligence and Machine Learning (AI/ML). SafriCloud surveyed SA’s leading IT decision makers to assess the sentiment and adoption outlook for these technologies amongst business and IT professionals. The results have been published in an eye-opening report entitled, ‘AI: SA – The state of AI in South African businesses 2021’. ‘Keen to start but facing a few challenges’ was the pervasive theme across the survey respondents, but with the global Machine Learning market projected to grow from $7.3 billion in 2020 to $30.6 billion by 2024*, why do we still see resistance to adoption? Nearly 60% of respondents said that their business supports them in their desire to implement AI/ML and yet only 25% believed that it is understood well at an executive level. While ‘fear of the unknown’ ranked in the top three adoption challenges both locally and internationally (Gartner, 2020), only 9.34% of respondents cited ‘lack of support from C-suite’ as a challenge. There is a clear degree of pessimism to the level of skills and knowledge to be found in the South African market. This pessimism is more exaggerated at a senior management level where more than 60% rated ‘low internal skill levels’ as the top challenge facing AI/ML adoption. With nearly 60% of the respondents rating the need to implement AI/ML in the next two years as ‘important’ to ‘very important’ and only 35% of businesses saying they currently have internal resources focused on AI/ML, the skills gap will continue to grow. Artificial Intelligence and Machine Learning represent a new frontier in business. Like previous generations that faced new frontiers – such as personal computing and the industrial revolution – we can’t predict what these changes might lead to. All we can really say is that business will be different, jobs will be different and how we think will be different. Those open to being different will be the ones that succeed. Get free and instant access to the full report, to discover whether your business is leading the way or falling behind: https://www.safricloud.com/ai-sa-the-state-of-ai-in-south-african-businesses/ Report highlights include: The areas of AI/ML that are focused on the most. The state of the AI job market and how to hire. Practical steps to train and pilot AI/ML projects.

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Ethiopian Airlines

7th December 2023 No Comments

Improves service deliveryand monetizes website window shoppers Unrivaled in Africa for efficiency and operational success, Ethiopian Airlines serves 127 international and 22 domestic destinations. Like many airlines, the company is always looking to lower costs and improve margins. Growing direct sales by capturing every potential booking from callers and website visitors is vital to meeting those goals. However, the airline’s contact center struggled with incompatible systems and information islands. Calls were routed to agents without taking into account language skills or competencies. That raised abandon rates, transfers and hand offs. Teams worked in silos using email and chat. There was no CRM system or workforce management; data resided on a central booking system or was buried elsewhere. The company lacked a full overview of the customer journey and real-time insight into conversations and preferences. The first step in the transformation was to replace an externally hosted contact center solution with a strong omnichannel platform that the company could manage internally and use to drive improvements and business growth. Live after two months, the Genesys Cloud™ contact center allows up to 500 agents to work more productively in a blended fashion, effortlessly switching between calls, email and chat conversations — all managed from a single omnichannel desktop. Introducing Genesys Workforce Management further improved the customer experience. As a result, Ethiopian Airlines has seen service levels soar from 70% to 95%, with higher first-call resolution and sizeable reductions in abandoned calls (from 20% to 3%). Call-answer times have dropped from 20 to 8 seconds. With two weeks of implementing Genesys Predictive Engagement, the airline not only gained insights about website journeys, it also leveraged artificial intelligence (AI) and analytics to uncover behaviors and interests of visitors. This allowed the company to offer tailored deals through webchat. Ethiopian Airlines also can engage customers through the website with attractive travel packages that were created as a result of tracking real-time statistics and data. Benefits 25% increase in service levels 60% faster call response 17% fewer abandoned calls 49% increase in website sales conversions 72% reduction in website dwell time Effective pandemic response without adding headcount Future roadmap for mobile and AI integration “Genesys Predictive Engagement is enabling us to capture significantly more window shoppers on our website. Conversion rates rose by 14% in the first two weeks and by 49% at the six-week stage. And, we’ve only really scratched the surface of what the tool can do.” Getinet Tadesse, CIO, Ethiopian Airlines Download AI success stories ebook https://www.genesys.com/resources/improve-customer-satisfaction-sales-and-workforce-engagement-with-genesys-blended-ai

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Artificial Intelligence and Machine Learning for robust Cyber Security

7th December 2023 No Comments

Machine Learning Africa recently partnered with Darktrace to present a webinar on Leveraging AI & Machine Learning for robust Cybersecurity. Topic: Leveraging Artificial Intelligence and Machine Learning in building robust Cyber security solutions. The adoption of emerging technologies comes with increasing cybersecurity risks. AI and ML can be used to detect and analyze cyber-security threats effectively at an early stage. Warren Mayer, Alliances Director for Africa at Darktrace, provided invaluable insight on the importance of self-learning and self-defending networks in mitigating cyber security risks. WATCH THE WEBINAR ON DEMAND

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How is Coding Used in Data Science & Analytics

7th December 2023 No Comments

What is Data Science? In recent years the phrase “data science” has become a buzzword in the tech industry. The demand for data scientists has surged since the late 1990s, presenting new job opportunities and research areas for computer scientists. Before we delve into the computer science aspect of data science, it’s useful to know exactly what data science is and to explore the skills required to become a successful data scientist. Data science is a field of study that involves the processing of large sets of data with statistical methods to extract trends, patterns, or other relevant information. In short, data science encapsulates anything related to obtaining insights, trends, or any other valuable information from data. The foundations of these tasks originate from the fields of statistics, programming, and visualization. In short, a successful data scientist has in-depth knowledge in these four pillars: Math and Statistics: From modeling to experimental design, encountering something math-related is inevitable, as data almost always requires quantitative analysis. Programming and Database: Knowing how to navigate program data hierarchies, or big data, and query certain datasets alongside knowing how to code algorithms and develop models is invaluable to a data scientist (more on this below). Domain Knowledge and Soft Skills: A successful and effective data scientist is knowledgeable about the company or firm at which they are working and proactive at strategizing and/or creating innovative solutions to data issues. Communication and Visualization: To make their work viable for all audiences, data scientists must be able to weave a coherent and impactful story through visuals and facts to convey the importance of their work. This is usually completed with certain programming languages or data visualization software, such as Tableau or Excel. Does Data Science Require Coding? Short answer: yes. As described in points 2 and 4, coding plays a significant role in data science, making appearances in almost every step of the process. Though, how is coding utilized in every step of solving a data science problem? Below, you’ll find the different stages of a typical data science experiment and a detailed account of how coding is integrated within the process. It’s important to remember that this process is not always linear; data scientists tend to ping-pong back and forth between different steps depending on the nature of the problem at hand. Preplanning and Experimental Design Before coding anything, it’s necessary for data scientists to understand the problem that is being solved and the desired objective. This step also requires data scientists to figure out which tools, software, and data be used throughout the process. Although coding is not involved in this phase, it can’t be skipped, as it allows a data scientist to keep his or her focus on their objective and not let white noise or unrelated data or results to distract. Obtaining Data The world has a massive amount of data that is growing constantly. In fact, Forbes reports that humans create 2.5 quintillion bytes of data daily. From such vast amounts of data arise vast amounts of data quality issues. These issues can be anything, ranging from duplicate or missing datasets and values, inconsistent data, misentered data, or even outdated data. Obtaining relevant and comprehensive datasets is tedious and difficult. Oftentimes, data scientists use multiple datasets, pulling the data they need from each one. This step requires coding with querying languages, such as SQL and NoSQL. Cleaning Data After all the necessary data is compiled in one location, the data needs to be cleaned. For example, data which is inconsistently labeled “doctor” or “Dr.” can cause problems when it is analyzed. Labeling errors, minor spelling mistakes, and other minutiae can cause major problems along the road. Data scientists can use languages like Python and R to clean data. They can also use applications, such as OpenRefine or Trifecta Wrangler, which are specifically made to clean data and transform it into different formats. Analyzing Data Once a dataset is clean and uniformly formatted, it is ready to be analyzed. Data analytics is a broad term with definitions that differ from application to application. When it comes to data analysis, Python is ubiquitous in the data science community. R and MATLAB are popular as well, as they were created to be used in data analysis. Though these languages have a steeper learning curve than Python, they are useful for an aspiring data scientist, as they are so widely used. Beyond these languages, there are a plethora of tools available online to help expedite and streamline data analysis. Visualizing Data Visualizing the results of data analysis helps data scientists convey the importance of their work as well as their findings. This can be done done using graphs, charts, and other easy-to-read visuals, which can allow broader audiences to understand a data scientist’s work. Python is a commonly used language for this step; packages such as seaborn and prettyplotlib can help data scientists make visuals. Other software, such as Tableau and Excel, are also readily available and are widely used to create graphics. Programming Languages used in Data Science Python is a household name in data science. It can be used to obtain, clean, analyze, and visualize data, and is often considered the programming language that serves as the foundation of data science. In fact, 40% of data scientists who responded to an O’Reilly survey claimed they used Python as their main coding language. The language has contributors that have created libraries solely dedicated to data science operations and extensions into artificial intelligence/machine learning, making it an ideal choice. Common packages, such as numpy and pandas, can compute complex calculations with matrices of data, making it easier for data scientists to focus on solutions instead of mathematical formulas and algorithms. Even though these packages (along with others, such as sklearn) already take care of the mathematical formulas and calculations, it’s still important to have a solid understanding of said concepts in order to implement the correct procedure through code. Beyond these foundational packages, Python also

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Fear of the Unknown: Artificial Intelligence

7th December 2023 No Comments

Artificial Intelligence (AI) will be the most popular and developed technological trend in 2020 with a market value projected to reach $70 billion. AI is impacting several areas of knowledge and business, from the entertainment sector to the medical field where AI is utilizing high-precision algorithms through machine learning that can produce more accurate diagnoses and detect symptoms of serious diseases at a much earlier stage. The innovation that AI offers to industry, businesses, and consumers is positively changing all processes. The new decade will be driven by the rise of automation and AI-induced robotics. However, there is a huge exaggeration and hysteria about the future of Artificial Intelligence and how humans will need to adapt and get used to living with it. In fact, AI is a topic that has polarized popular opinion. What is true is that AI will become the core of everything that humans interact within the coming years, and beyond. Hence, to have a clear opinion about AI and its impact, it is important to understand what it is and what are the types of artificial intelligence that exist. General Artificial Intelligence (AGI) is the type of AI that can perform any cognitive function in the way a human does. The technology is not there yet but it is developing at a fast pace and there are interesting AI projects such as Elon Musk’s Neuralink.  Today, narrow AI applications, intended to develop only one task, such as IBM Watson, Siri, Alexa, Cortana, and others are the ones that share the world with us. The key difference between the AGI or wide artificial intelligence and the narrow or weak AI is the goal setting and the volition. In the future, AGI will have the ability to reflect on its own objectives and decide whether to adjust them or not and to what extent. We have to admit that, if done right, this extraordinary technological achievement will change humanity forever. However, there is still a long way to go to get to that point. Despite this, many fear that Super Artificial Intelligence (ASI) will one day go beyond human cognition, also known as the technological singularity. At the moment, in society, there are two emerging and visible groups: on the one hand, the public is informed- in this group, trust towards new and emerging technologies has been increasing over time. On the other hand, there is the mass population -a group where trust remains stagnant. Of course, social networks also play a role here. It’s not just about consumption, but about amplification, with people who share news more than ever and discuss issues relevant to them. Confidence used to be from top to bottom, but now it is established horizontally from equal to equal. Will AI benefit or destroy society? AI can only become what humans want it to become. Humans have the task of coding their AI creations. If the mass population is increasingly anxious about AI, this is due to fear of the unknown. Perhaps it is also because there is very little information available about the benefits AI offers to balance with those who believe that AI will destroy society and take away their jobs. For now, AI has only been providing great benefits and its coverage in the medium term can only benefit and optimize many areas of human activity.  

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Python vs. Java: Uses, Performance, Learning

7th December 2023 No Comments

In the world of computer science, there are many programming languages, and no single language is superior to another. In other words, each language is best suited to solve certain problems, and in fact there is often no one best language to choose for a given programming project. For this reason, it is important for students who wish to develop software or to solve interesting problems through code to have strong computer science fundamentals that will apply across any programming language. Programming languages tend to share certain characteristics in how they function, for example in the way they deal with memory usage or how heavily they use objects. Students will start seeing these patterns as they are exposed to more languages. This article will focus primarily on Python versus Java, which are two of the most widely used programming languages in the world. While it is hard to measure exactly the rate at which each programming language is growing, these are two of the most popular programming languages used in industry today. One major difference between Python and Java is that Python is dynamically typed, while Java is statically typed. Loosely, this means that Java is much more strict about how variables are defined and used in code. As a result, Java tends to be more verbose in its syntax, which is one of the reasons we recommend learning Python before Java for beginners. For example, here is how you would create a variable named numbers that holds the numbers 0 through 9 in Python: numbers = [] for i in range(10): numbers.append(i) Here’s how you would do the same thing in Java: ArrayList numbers = new ArrayList(); for (int i = 0; i < 10; i++) { numbers.add(i); } Another major difference is that Java generally runs programs more quickly than Python, as it is a compiled language. This means that before a program is actually run, the compiler translates the Java code into machine-level code. By contrast, Python is an interpreted language, meaning there is no compile step. Usage and Practicality Historically, Java has been the more popular language in part due to its lengthy legacy. However, Python is rapidly gaining ground. According to Github’s State of the Octoberst Report, it has recently surpassed Java as the most widely used programming language. As per the 2018 developer survey, Python is now the fastest-growing computer programing language. Both Python and Java have large communities of developers to answer questions on websites like Stack Overflow. As you can see from Stack Overflow trends, Python surpassed Java in terms the percentage of questions asked about it on Stack Overflow in 2017. At the time of writing, about 13% of the questions on Stack Overflow are tagged with Python, while about 8% are tagged with Java! Web Development Python and Java can both be used for backend web development. Typically developers will use the Django and Flask frameworks for Python and Spring for Java. Python is known for its code readability, meaning Python code is clean, readable, and concise. Python also has a large, comprehensive set of modules, packages, and libraries that exist beyond its standard library, developed by the community of Python enthusiasts. Java has a similar ecosystem, although perhaps to a lesser extent. Mobile App Development In terms of mobile app development, Java dominates the field, as it is the primary langauge used for building Android apps and games. Thanks to the aforementioned tailored libraries, developers have the option to write Android apps by leveraging robust frameworks and development tools built specifically for the operating system. Currently, Python is not used commonly for mobile development, although there are tools like Kivy and BeeWare that allow you to write code once and deploy apps across Windows, OS X, iOS, and Android. Machine Learning and Big Data Conversely, in the world of machine learning and data science, Python is the most popular language. Python is often used for big data, scientific computing, and artificial intelligence (A.I.) projects. The vast majority of data scientists and machine learning programmers opt for Python over Java while working on projects that involve sentiment analysis. At the same time, it is important to note that many machine learning programmers may choose to use Java while they work on projects related to network security, cyber attack prevention, and fraud detection. Where to Start When it comes to learning the foundations of programming, many studies have concluded that it is easier to learn Python over Java, due to Python’s simple and intuitive syntax, as seen in the earlier example. Java programs often have more boilerplate code – sections of code that have to be included in many places with little or no alteration – than Python. That being said, there are some notable advantages to Java, in particular its speed as a compiled language. Learning both Python and Java will give students exposure to two languages that lay their foundation on similar computer science concepts, yet differ in educational ways. Overall, it is clear that both Python and Java are powerful programming languages in practice, and it would be advisable for any aspiring software developer to learn both languages proficiently. Programmers should compare Python and Java based on the specific needs of each software development project, as opposed to simply learning the one language that they prefer. In short, neither language is superior to another, and programmers should aim to have both in their coding experience. Python Java Runtime Performance Winner Ease of Learning Winner Practical Agility Tie Tie Mobile App Development Winner Big Data Winner This article originally appeared on junilearning.com

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ML Africa provides insight into emerging machine learning technologies and their inevitable impact in transforming Africa. Provides a platform where innovators, technology vendors, end users and enthusiasts discuss latest innovations and technologies that transform businesses and the broader society. 

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