An interesting aspect of the discussion around Artificial Intelligence (AI) is that the definition of the word continues to change as we accommodate and mainstream new technologies. For example, voice assistants, live translations and driving capabilities that are now in use are often considered pedestrian and not ‘real AI’. In short, once researchers solve a problem it is no longer considered AI but rather a mere computation.

There is no straightforward definition of artificial intelligence then; it is most easily understood as the attempt to develop computing systems capable of performing tasks that otherwise would require human intelligence.

AI is essentially an encompassing concept that holds within its mandate multiple, often overlapping disciplines. These draw upon knowledge and processes from statistics, mathematics, computer science and other specialized expertise to create models, software programs and tools.

Companies like Apple, Amazon, Microsoft, Google and Facebook are all employing AI technologies like deep learning, machine learning and language processing to provide new and improved experiences across their services. They have been acquiring AI startups which target different capabilities and thus have developed technologies like Siri, Alexa, Google Assistant and Google Lens, all available on consumer devices.

Based on these acquisitions, Apple launched FaceID, which was a facial recognition technology for securing smartphones. This technology is now available in most 2020 flagship mobile products.

Google has been dedicating enormous resources to its AI capabilities and has research areas ranging from Education to Machine Intelligence to Quantum Computing. Recently the company also invested in six AI based research projects in India that focus on a variety of problems like environmental challenges, healthcare and education.

Financial institutions and hospitals have started utilising AI systems for fraud detection and diagnosis of diseases to effectively harness the potential of their information and tackle more complex problems.

Some deep learning techniques are being used for video analytics that can help clients in sectors such as energy, real estate and maritime by providing real-time alerts for detecting intrusions, evaluating traffic flow density, facial and character recognition for law enforcement services.

The applications of AI are multiple including:

• The challenges that supply chain management faces without the help of computational power can vary from lack of visibility, weak links, matching demand with production and regulation of suppliers.
• AI programs can control and respond automatically to scale supply chains in response to real-time or predicted demands. Machine learning and IoT devices are also looped in on the information exchange where intelligent monitors can automatically pinpoint failing links in the supply chain.
• AI can also make online retail experience more intuitive and compelling. Analytic systems can explore existing customer graphs to find new customers and virtual sales agents while predictive algorithms can engage with customers to increase sales.
• In managing operations, Artificial Intelligence programs can estimate the probability of failure in production components. Machine vision also provides automated visual inspections in which personnel systems can learn from employee data and past performance to allocate the best available employees.
• The IT industry also uses self-learning algorithms to predict and prevent maintenance requests.

It is no surprise then that the Indian landscape is also booming with AI research and startups. A few major examples of these are:

1. Staqu – A Gurugram-based startup that specializes in security software to help mitigate crime. It employs an AI-based facial recognition technology to detect criminals in crowded environments. A more sophisticated version of this is China’s big-data police system called Skynet.
2. Blue Sky Analytics – Provides real-time environmental data by using AI which is high resolution and high frequency in response to air pollution. They also provide air quality predictions for the next three days along with comparisons.
3. Intello Labs – Works with the agricultural industry to provide solutions such as image detection, analysis, grading and testing of commodities such as corn, tomatoes, wheat, potatoes, onions and soybeans.
4. Leverage Edu – An AI startup that seeks to provide educational mentoring and guidance for students through college admission processes. This application is available to professionals as well.
5. Niramai – Uses a technology it calls ‘Thermalytix’ which is a thermal sensing device that scans the chest area like a camera (and solves the problems of privacy and touch). Once the scan is produced to a pathologist, early signs of cancer can be detected.

With the increased implementation of automation and AI in organisations, there have also been concerns regarding potential unemployment. However, proponents of the technology which has now found an almost irreversible place in everyday life, argue that AI could be leveraged in businesses to boost productivity and create a whole new set of opportunities, thus, not making jobs obsolete but changing their requirements.

Perhaps the bounds of computing capabilities cannot be set. However, for now, they remain defined by the technical challenges we must overcome to achieve true artificial intelligence. The rapidly evolving field is surging rapidly to penetrate all sectors of industry, which, if used empathetically and reasonably, has the potential to change global social and economic interactions for the better.

This blog was authored by Kartikeya Saigal.