Kristina Somolyar

NEXT BIG THING AFTER AI

The term artificial intelligence (AI), which has become popular in recent years, has completely changed how we live and work. But even as AI develops and gets better, there are already new technologies on the horizon that have the potential to overtake it as the next big thing.

1. Augmented Intelligence: The term “augmented intelligence” describes the application of AI methods to improve human intellect and judgement. Decision-making could become more efficient as a result of this strategy, especially in industries like healthcare and banking. We can outperform either intellect alone by combining its strengths with those of human intelligence.
2. Quantum Computing: The processing power and speed of AI systems could be significantly improved by quantum computing, an emerging field. As a result, new AI uses in fields like cryptography and drug discovery may be possible, as well as the creation of more sophisticated and precise AI models. Impressive outcomes from quantum computing have already been seen in fields like optimisation and machine learning.
3. Explainable AI: An approach to creating AI systems that can communicate their thought and decision-making processes in a manner that humans can comprehend is known as explainable AI (XAI). As AI is used in crucial uses like healthcare and finance, this is becoming more and more significant. We can make sure that AI systems are open and responsible, and that their judgements can be trusted, thanks to XAI.
4. Neuromorphic Computing: A method to creating AI systems that is based on the structure and operation of the human brain is known as neuromorphic computing. This might make it possible to create AI systems that are more intelligent and flexible and that can learn and adjust instantly. The creation and development of AI systems could undergo a radical change thanks to neuromorphic computing, which also has the potential to significantly enhance the functionality and performance of these systems.
5. Edge Computing: The use of computing tools and AI algorithms at the network’s periphery, nearer the data source. This could lead to real-time data processing that is more effective and efficient as well as new AI uses in fields like the Internet of Things. (IoT). By drastically reducing the quantity of data that must be transmitted to the cloud, edge computing can increase the speed and effectiveness of AI systems.
6. Swarm Intelligence: Swarm intelligence is an emerging field that investigates the group behaviour of autonomous, decentralised systems. This method can be used to create AI systems that are more resilient and robust, as well as versatile and adaptable. We can create artificial intelligence (AI) systems that can solve complex problems and adjust to changing environmental conditions and by studying behaviours. 

These are just a few examples of what may come after AI. While AI has been a transformative technology, it is still in its early stages, and there is much more to be discovered and developed. As AI continues to evolve, we can expect to see new applications and use cases that we cannot even imagine today. Ultimately, the future of AI will be shaped by ongoing advancements in machine learning, natural language processing, computer vision, and other core AI technologies, as well as by the creative and innovative ways that people use these technologies to solve real-world problems.

WILL AI DISRUPT CONVENTIONAL PRODUCT MANAGEMENT?

Product management has not been exempt from the disruption caused by artificial intelligence (AI). It’s not surprising that AI is playing a bigger role in product management given its capacity to automate various tasks, forecast future trends, and analyse vast amounts of data. Here are some future methods that AI will influence product management:

1. Improved decision-making – Large-scale data analysis performed by AI programmes can yield insights that support the decision-making of product managers. Predicting consumer behaviour and preferences, identifying market trends, and analyzing competitor statistics all fall under this category. Product managers can reduce the risks involved in product development by using AI to make data-driven choices.
2. Personalization – Product managers can use AI to develop customised products for their customers. AI can offer product managers insights on how to tailor the product to the customer’s particular requirements by analysing customer data, such as their preferences and behaviours. This can involve making specific product recommendations, setting specific prices, or even creating unique packaging.
3. Enhanced customer experience – By making tailored recommendations and anticipating customer requirements, AI can assist product managers in improving the customer experience. For instance, chatbots powered by AI can offer customers immediate assistance, minimising the need for human involvement. This could boost client loyalty and customer satisfaction.
4. Streamlined product development – AI can speed up the creation of new products by streamlining a variety of processes, such as quality control and product testing. This can guarantee that products are launched more quickly and cost-effectively while also cutting down on the time and expense involved in product development.
5. Increased efficiency – By automating repetitive duties like data analysis, report generation, and project management, AI can help product managers work more efficiently. Product managers may find it easier to concentrate on more strategic activities like product development and customer involvement as a result.
6. Improved supply chain management – AI can assist product managers by analysing data and locating possible bottlenecks in their supply chains. This may reduce the chance of inventory stockouts and ensure that products are delivered to customers on time.

In conclusion, AI will transform product management in a variety of ways, from enhancing client experience to enhancing decision-making. To stay competitive in the market as AI develops further, product managers will need to adapt and incorporate it into their product development processes.

VERTICAL FARMING = SCALABLE FARMING FOR TOMORROW

You must have come across the term ‘vertical farming‘ throughout the years. The technology began with a farming method known as hydroponics, sometimes also known as farming without soil, in which the roots are grown in a liquid nutrition solution and are grown vertically to save space. Greenhouses were utilized since these plants could be exposed to excessive cold or heat, which would be detrimental to their growth. How scalable is vertical farming?

Vertical farming has been scaled thanks to the application of Artificial Intelligence (AI) TECHNOLOGY, and it has piqued the interest of many countries concerned with food supply and environmental impact reduction.

How does it work?

Yes, we can now produce strawberries in the Sahara Desert.

Vertical farming uses three general processes:

User Demand

Vertical farming is no longer an explored option in many regions of the world; it has become a necessity to meet supply and demand. Vertical farming provided solutions to issues such as:

• All year production
• Unaffected by weather
• Better/maximize use of space
• Minimize usage of water
• Environmentally friendly
• Absolutely no chemicals or pesticides used
• Reduced transportation costs
• Energy saving
• Lower labour cost
• Safer working environment

Market Size

According to Grand View Research, the current market size of vertical farming is USD 5.37 billion in 2022, with an anticipated market size of USD 33.02 billion by 2030.

Is the Technology ready for an average user/farmer?

I believe it is more a matter of financial preparation than of readiness or how tech-savvy a user or farmer is. Is the initial cost affordable to the typical user or farmer? While technology advances on a daily basis, a few technology companies have developed vertical farming management solutions that automate the process and require very little human intervention on a daily basis.

According to Infosys one of the largest Technology companies in the world, the need to automate or manage vertical farming is no longer a concern; instead, economies of scale is what will propel vertical farming forward.

VSNi statistical software built by industry experts, which has decades of experience supporting agriculture industries with volume production, could be the solution to this problem.