Companies across industries are getting overwhelmed by data and its fuzziness, the challenge is how to transform the complex data into intelligence which would help drive businesses competitive advantage.
AI learns through trial and error, and that requires massive amounts of data to teach the Artificial Intelligence, however AI doesn’t draw conclusions by themselves like humans do, therefore “human cognitive expertise and judgement” is critical to make sense of data.
Big Data is the raw input that needs to be cleaned, structured and integrated before it becomes useful, while Artificial Intelligence is the output, the intelligence that results from the processed data.
Artificial Intelligence is a form of computing that allows machines to perform cognitive functions, such as acting or reacting to input, similar to the way humans do.
The Developing Economies, Mobile & Data
In the developing world small and medium enterprises are not yet ready to understand data or prepared to invest to understand its importance. However the landscape is changing rapidly.
The increase in mobile phone use has also led to an increase in data, creating opportunities for data scientists to make life-altering insights into these numbers. Scientists working with the UN WFP have discovered that there are strong correlations between mobile phone data usage like how many airtime credit minutes users purchase and food consumption.
The more data people use on their phones, the more likely they are to purchase quality foods. This is an interesting insight indeed. According to researchers, is that proxies derived from mobile phone data could be used to provide valuable up-to-date operational information on food security throughout low and middle income countries.
Researchers were able to determine the current and future electricity needs based on cell phone data, similar to the food project mentioned above.
Data was also used to build granular poverty maps, which showed the exact locations of pockets of poverty that no one noticed were there before.
Additionally, the challenge showed that data can be used to determine the ideal locations and routes for new roads based on phone usage patterns, and create a very detailed model of a population using indicators such as population, age, literacy, poverty, religion, and ethnicity.
Artificial Intelligence doesn’t draw conclusions like humans do. Data must be processed with advanced tools analytics, human judgement and algorithms to reveal meaningful information.
Malcolm Gladwell
Best-selling author and journalist Malcolm Gladwell says people often wondered if the nascent role of automation and machine learning would replace human decision making, and he believes they do not. In the future we are much more in need of human judgement than ever before.
Gladwell challenged the FutureStack: New York audience — “people who live in the world of numbers” — to occasionally take the time to ask skeptical questions of the data they work with, to learn the stories behind the numbers, to ask “dumb” questions that challenge conventional wisdom. Otherwise, he said, “all the metrics in the world won’t make your jobs any easier.”
Malcolm Gladwell believes that an expert in the future will not be a puzzle solver, or someone who goes about gathering information. It will be someone practised in the incredibly complex art of making sense of complexity.
“It’s worth it to take a step back and be skeptical about how we use the data we get,” Gladwell said, to spend some time “interrogating the meaning of the numbers.” People often think that data is inherently objective, he pointed out, but the numbers always come with history, ideology, and implied hierarchies that impact their value and the conclusions we draw from them. “There are implicit ideologies and philosophies behind the numbers we use,” he said, and we need to understand them in order to avoid mistakes in how we use them.
The fourth industrial revolution is unique in that the technology powering new industry has been completely democratized.
The next Uber
Entrepreneurs only need an idea and knowhow to develop the next Uber — and the global nature of venture capital. This era a global phenomenon with contributions from all over the world.
The Silicon Valley will of course do a lot of the heavy lifting, but startups and tech giants in China, Singapore, Japan, South Korea, Taiwan and India have already made remarkable inroads in various technology segments and are increasingly pushing the envelope as the benefits of being part of this journey become clearer with each innovation.
AI’s evolution is currently being steered by the exponential growth in computing power and the solid cloud and smart device ecosystem in place. Favorable supply factors, like low computing and storage costs, advanced algorithms and the increased availability of AI-based talent, are also helping to nurture the necessary conditions for progress.
Like all technologies, AI will be created with a singular purpose: to aid humanity. The Hollywood need not fear the rise of the machines.
Exoskeletons
The days of Artificial Narrow Intelligence (ANI) will be all but forgotten as Artificial General Intelligence (AGI) becomes the industry standard across all sectors.
The adoption of AI in healthcare could improve the quality of life for millions and could save lives that otherwise wouldn’t be possible.
Medical analytics will ultimately transform healthcare delivery as billions of clinical records and images are analyzed and fed into AI algorithms. These programs, supported by mobile health applications that yield real-time data, will provide diagnosis and treatment recommendations customized to the patient’s medical history.
This should greatly reduce the element of human error in medical treatment in general. If surgery is needed, intelligent automation will be able to conduct increasingly complex operations as the technology develops. Already, robotic systems are playing a vital role in helping in the field of orthopedics, an area set to boom in the coming years.
The first efforts to bring exoskeletons into the mainstream saw them employed to help people with disabilities walk again.
Main components and ecosystem of Big Data
- Techniques for analyzing data, such as A/B testing, machine learning and natural language processing
- Big data technologies, like business intelligence, cloud computing and databases
- Visualization, such as charts, graphs and other displays of the data
— McKinsey Global Institute
AI definition in context
Artificial Intelligence is about decision making, and learning to make better decisions. Whether it is self-tuning software, self-driving cars or examining medical samples, AI is doing tasks previously done by humans but faster and with reduced errors.
Big Data definition
The term has been in use since the 1990s. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big data was originally associated with three key concepts: volume, variety, velocity and veracity.
- Volume: The quantity of generated and stored data. The size of the data determines the value and potential insight, and whether it can be considered big data or not.
- Variety: The type and nature of the data. This helps people who analyze it to effectively use the resulting insight. Big data draws from text, images, audio, video; plus it completes missing pieces through data fusion.
- Velocity: In this context, the speed at which the data is generated and processed to meet the demands and challenges that lie in the path of growth and development. Big data is often available in real-time.
- Veracity: The data quality of captured data can vary greatly, affecting the accurate analysis.
Big data has simply become bigger than what human can handle on their own, particularly with global data stores. According to IDC, companies will be storing more than 100 trillion gigabytes of data by 2025, 10 times the amount created in 2016.
“A mystery is a problem you solve by making sense of the information that you already have. It is a problem caused by an excess of data, not a surfeit.”
With all that data, organizations now realize they not only can discover more about consumers, patients, markets, machinery, and anything else that generates data, but they also can predict their behavior and business impact on their traditional revenue models and future profitability.
Further, they can take actions that change outcomes for the better, whether reducing customer churn, finding cost efficiencies, or improving diagnosis accuracy which will indeed lead to probability.
Enterprises that will survive and thrive in this digital economy are those that have a pervasive strategy in place across data, analytics, and AI — the three interconnected categories of technology, along with infrastructure, that are fueling digital transformation.
Key to this transformation will be the use of autonomous analytics and Machine Learning, enabling enterprises to drive greater automation of tasks and derive insights at breakneck speed.
By 2021 Gartner projects that 40 percent of all new enterprise applications implemented by service providers will include AI technologies. And while AI is working its way into many software types, from chatbots to security systems, a game-changing area for its use will be big data analytics.
The use of analytics and AI is rapidly expanding, powering the personalized experiences contemporary consumers and business users demand. Taking the methodical approach can help organizations quickly and effectively realize the benefits of AI related to transforming traditional business intelligence and analytics.
“AI will not only reduce costs by automating processes but also maximize revenues by helping corporates introduce new product and service categories.”
Disclaimer: Ideas shared here are based on personal views and has no commercial value. Research Reference: The World Bank, World Economic Forum, Singularity Hub, New Relic, UBS. Photos: Unsplash, Wiki, Google.
Sandeep Ozarde 