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AI Tech Climate Tech

From Dirt to Dish: Rethinking Food Production and Consumption 🍽🔥

From Dirt to Dish: Rethinking Food Production and Consumption

Written by Marcus Parade on Digilah (Tech Thought Leadership)

𝐋𝐞𝐭 𝐮𝐬 𝐢𝐦𝐚𝐠𝐢𝐧𝐞 𝐰𝐞 have over 8 billion gorilla’s 🦍🦍 living on our planet – would you say we have an overpopulation of gorillas?

As I find overpopulation very visible on our planet, the question arises, how can we feed all our people more sustainable, if our population worldwide is estimated by the UNO to be around 10 billion by the year 2050?

𝐃𝐞𝐟𝐨𝐫𝐞𝐬𝐭𝐚𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐟𝐨𝐨𝐝:

𝐈 𝐰𝐚𝐬 𝐰𝐚𝐥𝐤𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐚 dear 𝐟𝐫𝐢𝐞𝐧𝐝 strolling through endless acres of golden cornfields stretching as far as the eye can see 🌽🌽

He said: “Oh Marcus, it feels so nice to be outside here in lovely nature?!”

I also love walking along golden cornfields and yet the fields feel to me only partly being in nature. Putting it baldly, we were also walking in simple production fields for farming products. We didn’t see animals nor insects.

🌲🌴 𝐃𝐞𝐟𝐨𝐫𝐞𝐬𝐭𝐚𝐭𝐢𝐨𝐧 𝐡𝐚𝐬 𝐛𝐞𝐞𝐧 going on steadily since the last 10.000 years and an end is not in sight. The crazy race for more farmland, driven by climate change and an ever-growing population, is a ticking time bomb that is likely to trigger ecological collapse on a global scale.

1st. circle is from 10.000 years ago. 2nd. circle from 300 years ago. 3rd. circle from 5 years ago.

The question is, how do we feed all people, as further deforestation for more farmlands can in my opinion not be the solution.

In turn, around 1/3 of all food produced for human consumption in the world is lost or wasted every year. Where does it all go?

It is lost during production or wasted at the consumer level. At the same time around 10 % of our world population are starving.

𝐖𝐡𝐚𝐭 𝐜𝐚𝐧 𝐰𝐞 𝐝𝐨 𝐛𝐞𝐭𝐭𝐞𝐫?

We can change our consumer behaviour, as the production such as for meat needs 𝐄𝐍𝐎𝐑𝐌𝐎𝐔𝐒 amounts of more energy, water, space as well as emissions into our atmosphere. AND, less than only 20% of our worldwide farmlands are used for the direct consumption by us humans.

A staggering 80% of our planet’s farmland is devoted to livestock feed, biofuels, and other non-food crops, leaving precious little for direct human consumption.

𝐖𝐞 𝐧𝐞𝐞𝐝 𝐛𝐞𝐭𝐭𝐞𝐫 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐢𝐞𝐬 𝐢𝐧 𝐨𝐮𝐫 𝐟𝐨𝐨𝐝 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐚𝐭 𝐚𝐥𝐥 𝐥𝐞𝐯𝐞𝐥𝐬.

New agricultural technologies can be game changers such as:

1.💦💧 More precise watering solutions directly reaching the roots

2. 🚜🛰 Better efficient machines with more precision farming

3. 🌱☣ Biotechnology with genetic modification of plants to protect them better and produce more. 𝐈𝐧 𝐚𝐝𝐝𝐢𝐭𝐢𝐨𝐧 bacteria can be created producing nutrients like proteins.

4. 💡🎲 Use of AI (artificial intelligence):

AI-powered 𝐝𝐫𝐨𝐧𝐞𝐬 𝐚𝐧𝐝 𝐫𝐨𝐛𝐨𝐭𝐬 🤖 𝐜𝐚𝐧 𝐛𝐞 your farm’s new best friends – they collect real-time data on crop yields, plant health, and soil moisture levels. It’s like having a personal assistant who knows everything about your plants.

Self-driving tractors 🚜 and drones can be equipped with 𝐀𝐈-𝐩𝐨𝐰𝐞𝐫𝐞𝐝 𝐬𝐞𝐧𝐬𝐨𝐫𝐬 𝐭𝐨 optimize crop planting, fertilizing, and harvesting. With their precision and efficiency, they can help reduce labour costs and increase productivity.

AI can be like the ultimate 𝐬𝐮𝐩𝐩𝐥𝐲 𝐜𝐡𝐚𝐢𝐧 𝐝𝐞𝐭𝐞𝐜𝐭𝐢𝐯𝐞 🕵. It can analyse all data from the very beginning up to table to help optimize logistics and reduce waste. It’s like having a personal assistant who knows everything about your plants and how they travel to us.

When it comes to crop monitoring, AI has a 𝐞𝐚𝐠𝐥𝐞 𝐞𝐲𝐞 👁👁 that even most farmers can’t beat.. AI peering deep into the heart 💚 of crops to detect even the slightest signs of disease or pests, empowering farmers to take proactive measures and safeguard their precious yields.

Around 70% of our freshwater is frozen, around 29% is groundwater and the rest is surface water. Source of chart: Netafim

𝐀𝐧𝐝 𝐰𝐢𝐥𝐥 𝐭𝐡𝐢𝐬 𝐛𝐞 𝐞𝐧𝐨𝐮𝐠𝐡?

Improving the production of food on our planet Earth 🌏 is a complex issue that involves various stakeholders including farmers, governments, researchers, and consumers.

While overpopulation remains one of our main challenges is the production of our food a central challenges for our common future of our climate and us humans.

Let’s embrace our future and change for better sustainability and life. 💛🌹🌞

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Categories
AI Tech

ChatGPT and Generative AI’s – A Digilah view

ChatGPT and Generative AI’s
ChatGPT and Generative AI’s

Written by Vidya Dhareshwar on Digilah (Tech Thought Leadership)

Chatgpt, Bard AI and all the generative AI seems to be the current flavour. There is an insurmountable buzz around and about it . 

Everyone has an opinion on its impact and the ways that it can change, how we and our future generations engage with and use tech in our daily lives.

Whilst there have been many concerns on its impact on search engines, students, jobs and livelihoods, the fact remains that this evolution has happened and is here to stay.

ChatGPT alone has been the fastest growing consumer internet app ever with over 100 million users two months after launch. This itself shows the vast potential of generative AI.

 Just as life evolves, so does technology and yet this revolutionary technology doesn’t take away from human intelligence instead it is trained to learn what humans mean when they ask a question. 

Many users are awed at its ability to provide human-quality responses, inspiring the feeling that it may eventually have the power to disrupt how humans interact with computers and change how information is retrieved.

In the context of Digilah, where we like to provide a digital platform for every tech enthusiast to learn and contribute their tech journey and thought leadership, we view chatGPT, Bard AI and all other generative AI’s as an enabler and an opportunity for many of our start up and tech founders to share their learnings.

Let’s talk about the tech startup market in South East Asia alone. As per a Forbes article, The digital and tech industries of this region have enjoyed an enormous boom over the last few years. 

According to Jungle Ventures, Southeast Asia’s technology startups had a combined valuation of $340 billion in 2020, and they anticipate this will triple by 2025.

This is a diverse but very strong prospective market with a focus in Vietnam, Thailand, Indonesia, Malaysia, Singapore and the Philippines.

This market is quite complicated. Many entrepreneurs are hindered by concerns over a difference in mentality and a lack of understanding of how to do business there. We @ Digilah look at this as a huge opportunity.

There is a need to get all of the learnings and journeys of these startups and founders so that this rich knowledge repertoire is available to all. 

Many of them would like to share their journeys and provide their insights but sometimes are busy learning and navigating the markets and business challenges and for some it might also mean a constraint in terms of resources and skills to share their journeys be it content creation or communication skills or just time.

We present the combined power of Human Experience with the generative AI’s in the form of articles published by us at Digilah. Our submission is to use the vast reach of the generative AI tools to start the journey.

What this tech will do is provide for a framework, a skeleton, a structure of an article , a startup founders journey as a start point. This can then be brought to life by adding the content  and context of experience, leadership, success, failures and insights by the tech founders.

These articles are extremely valuable and become a  rich database of insight and knowledge for all knowledge seekers today and for the future.

In short, in our view, ChatGPT, Bard AI AND Human Experience is the opportunity to build the knowledge here at Digilah, all at the click of a key.

Hello readers! Hope you liked what you read today. Click the like button at the bottom of this page and share insights with your colleagues and friends!

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Categories
Climate Tech

IoT For Sustainability

IoT For Sustainability

Written by – Agrim Nagrani on Digilah (Tech Thought Leadership)

The current fast paced world is creating new opportunities for growth every day. With emerging technology and an interconnected world via IoT, there is always room to create a new market and provide solutions for growth of older ones to create a more technologically advanced society. 

The fourth industrial revolution, more commonly referred to as Industry 4.0, is characterised by the use of smart technology developed by a combination of Machine Learning, Artificial Intelligence, Cloud Computing and Internet of Things, to analyse data in order to increase efficiency and user satisfaction.

The creation of smart factories and machines, opens up a realm of possibilities, providing for solutions in Manufacturing, supply chain management, modernising enterprise applications, edge computing, smart systems, 5G and many more. 

What exactly is Industry 4.0?

In a most generic way, Industry 4.0 describes a rapid shift to the use of automation and data exchange in technology and manufacturing processes, creating a system where machines rely on wireless connectivity and data analysis to monitor and process statistics and data to make autonomous decisions.

With the availability of affordable edge infrastructure and advanced connectivity technologies, Industry 4.0 has become increasingly mainstream, taking hold of the traditional manufacturing system and   completely revolutionising it.  

Heavily dependent on IoT, ML and AI, it has paved a way for the next revolution, one guided by robotic automation.

Why is IoT important?

The internet of things (IoT) is a computing concept that describes the idea of the network of physical objects embedded with sensors, software, and other technologies being connected to the internet and being able to identify themselves to other devices.

The data from IoT is important as it creates an improved customer experience, with greater efficiency in production and optimised monitoring and data analyses to further industrial growth.

With the changing world, the need for novel solutions is now greater, with more focus being put on the future, especially a self-sustainable one. 

Businesses need to rebrand themselves, or prove themselves to be at the head of the change as reliable information providers in order to keep up with the flow of the tides.

IoT entrepreneurs need a wide range of vision to cater to the needs of the market, both the current and future, and as such the IoT businesses can be generally categorised as core, adjacent or transformational depending on the type of services it provides.

IIOT and Industry 4.0

Industrial IoT or IIoT takes the concept of IoT and applies it on large scale industrial settings with focus on instrumentation and control of devices using cloud computing.

Use of Machine-to-Machine communication to achieve wireless control was a pre-existing working concept, however, with use of cloud computing and machine learning, a new level of automation can be achieved, thus leading to unprecedented growth in revenue and creation of new business models. 

Some of the more common uses of IIoT are: 

  • Smart manufacturing
  • Smart power grids 
  • Smart cities
  • Smart digital supply chains

While IIoT and Industry 4.0 are separate concepts, they do benefit each other and are considered as a set piece when working to increase efficiency in operation via automation.

Industry 4.0 itself is non-existent without IIoT while the concept of IIoT is inefficient without the concept of Industry 4.0 and as such, they share several common agendas.

  • Focus on results and efficiency to streamline production process and make manufacturing viable and cost efficient
  • Both categories heavily depend on high speed wireless communication between smart machines and constant real time monitoring and data analysis.
  • People Driven and requiring constant development and implementation with people capable of interpreting data to further innovate on faster efficient processes

Benefits of IIoT

Maximising revenue – By eliminating unplanned downtime and getting to market faster, improve revenue growth

Lower operational costs – Interconnected machines and industrial data boosts productivity while lowering cost of production

Improved Quality – Market analysis and monitoring via an interconnected system improves efficiency and quality of service/product

Creating a Brand

With the vast domain that is IoT, it is difficult to find a niche to hold onto and create a business model surrounding it.

In order to keep up with the booming growth, there is a need for constant influx of novel ideas and sharing information in order to create a brand image that is credible and also due to the recent nature of all developments and a lack of general awareness, it may be difficult to provide both efficiency and sustainability.

This is where IoTAGI comes in, enabling access to technology and partnering with Industry 4.0 based service providers to curate customised IIoT solutions and help usher in a new era of digitization.

Most searched question 

How IoT can help sustainability?

How will IoT impact sustainability of environment or business?

What impact will IoT have on sustainability?

What is industrial IoT?

Most searched queries

IoT sustainability PDF

Internet of Things environmental impact

IoT for environment

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Categories
Edu Tech

AI for Inclusive Education Personalized Learning systems

Written by Dr. Sandeep Bansal on Digilah (Tech Thought Leadership)

Everyone knows that different individuals possess different capabilities, comprehension abilities, problem-solving skills, and hence the learning needs vary across the students of a class with their varying interests, abilities, performance, pre-requisite knowledge, etc.

In order to address such needs, the adaptive/Personalized learning/teaching systems are being worked upon with the involvement of technologies like Artificial Intelligence, Machine Learning etc. These systems provide different learning paths with different paces to different learners based on their needs & performance but they use the same study material for all the learners; though it may be delivered to them at a different pace depending on their needs and performance; while the learners need study material as per their need, capabilities for the same topic. This gives a rise to the need of not only developing different study materials varying with variations in different parameters but developing the machine learning models also which would take care of not only the learning needs being dealt with by today’s system but other needs being discussed further here.  

Countries like India have lots of diversities with respect to language, culture, regions, etc. Such learning systems can play a key role to bring inclusivity in education as we note:  

  • Learners with physical disabilities (Divyang Jan): In India, around 1% of school-going children are children with physical disabilities and need transformation of e-content.
  • Learners with learning disabilities: In India around 5% of the school going children are affected with learning disabilities (dyslexia, dyspraxia, dyscalculia and dysgraphia etc.. Each type of disorder may coexist with another). In such cases also the study material needs transformation.
  • Learners with different socio-cultural identities, socio economic & geographical identities with the needs of study material in different languages, dialect & culture.

The fundamental principles of  NATIONAL EDUCATION POLICY (2020) of Indian Government include –

  • recognizing, identifying, and fostering the unique capabilities of each student
  • focus on regular formative assessment for learning
  • extensive use of technology in teaching and learning, removing language barriers,
  • increasing access for Divyang students, and educational planning and management

Therefore the role of AI based adaptive system delivering the right content at the right time to the learners in personalized manner has a very important role to play. The machine learning models need training data also for their adaption to the different learner’s need with regard to their learning attributes (like some emerging system) & varying needs of different content for the same topic (not common & to be evolved). How it all would work may be understood with a simple architecture of the whole system. The basic building blocks including the content would be as follows:

  1. Artificial Intelligence-based Decision System: This would be the core of the whole system to interface with the front-end i.e. learner’s interface, with the learner’s repository where the learner’s attributes would be stored and with the content repository where the content of different languages, formats, and levels to choose from for the learners would be stored.
  • Learner Interface: It provides the test material to the learners and based on performance captures different attributes of the learners e.g. learning disabilities, problem-solving skills, comprehension abilities, misconceptions, gaps in prerequisite knowledge which are then sent to the “Learner’s Repository” module. The whole process is controlled by the core AI-based Decision system. The interface may be equipped with conversational AI (powered by Natural Language Processing, Speech recognition to interpret the intent of the user and providing smooth interaction with the system) in his/her language.
  1. Content Repository: Different type of content e.g. Content for physical disabilities, Content for learning disabilities (the content of different levels of explanation with a provision of need-based detailing of basics & prerequisites involved in the concept to enable the learner to drill down), The content to address the needs of study material in different languages, dialect & culture etc.
  • Learner’s module: The attributes of the learners captured by the AI system through the learner’s interface would be stored in this repository consisting of attributes of different learners with their respective learning attributes and learning paths to be followed by them.

Learning Path (sub module of learner’s module): To decide on the appropriate learning path for the learner, the system first evaluates the learners with respect to different attributes, different learning issues etc.. For example a learner first needs to understand the concepts of Motion to understand “Simple Harmonic Motion”, then waves, then Light’s concepts, then concepts of reflection & refraction and so on. During assessment of the learner, if it is found by the AI system that the learner is lacking somewhere, the system would advise the learner to go back to the basics.

Content Selection by the Decision system: The content selection logic of the system would take care of the content selection at all stages from the content repository based on different attributes & need of the learners. Based on various factors of the learner’s as summarised below, the AI based system would choose the appropriate content for the learner.

For the proposed system the availability of content for training the models & e-content for the learners in different formats is a bigger challenge. The initiatives of the Government of India e.g. Natural Language Technology Missions (targeting content in Indian languages), guidelines, policies for e-content development (including those for children with special needs) by Ministry of Education, Ministry of Social Justice & Empowerment would play a key role to make such content available. As an outcome a lot of content is expected to be made available in different languages & formats which may be used for such solutions.

Categories
Decision Making Tech

Better than Before: Making sense of data in an age of information overload

Written by Ira Gilani Lal  on Digilah (Tech Thought Leadership)

In a 2016 Harvard Business Review article, Scott Anthony shared some insights from a study on S&P 500 companies:

  • 61-year tenure for average firm in 1958 narrowed to 25 years in 1980 – to 18 years in 2012
  • At current churn rate, 75% of the S&P 500 companies will be replaced by 2027

Business leaders commonly refer to the military acronym VUCA (Volatility, Uncertainty, Complexity, Ambiguity) to describe the world today. The external environment is changing at a rapid pace and companies cannot afford to be caught off guard. How can companies continue to thrive, in this ever-changing external environment? While there are several challenges, and there are also plenty of opportunities. Deep-rooted assumptions hold us back from unlocking this hidden potential.

Today’s information and digital systems are capable of providing a huge amount of data at the click of a button. Most organizations measure a large number of metrics for each business unit, division, department, employee level etc. The underlying assumption is that the more we measure, better we are! Most senior executives are quite familiar with their local measurements (e.g. tons, units produced, order book, number of subscribers etc.) but are ignorant of the overall financial measurements. 

Everyone in the company should understand financials; it is not just for Accounts or Finance function. In most organizations, the top management team does not have a good understanding of Free Cash Flow. In his book, Conspiracy of Fools, Kurt Eichenwald writes that in 2001, just a month before the collapse of Enron, its chairman Kenneth Lay, CEO Jeffery Skilling, and CFO Andrew Fastow did not know that Enron would run out of cash in a matter of weeks!

Dr. Eli Goldratt, author of the best-selling book The Goal, repeatedly emphasized that “Measurements Drive Behavior!”. The purpose of measurements is to take decisions for corrective actions. At the organization level, a few simple parameters are good enough. Timely data and corrective actions can help individuals to connect the dots and see the big picture.

Most companies review performance monthly. This leads to a significant time lag in getting key data or MIS. We recommend a weekly review mechanism with focus on 3-5 key metrics. The objective of the review is only to take decisions for corrective action. The weekly report should be simple and accurate, leaving no room for analysis paralysis, and facilitating effective decision-making.

Increasing digitization of data across the organization has been a key enabler for running the weekly reviews successfully. Companies that have adapted this methodology, provide a very high degree of focus on getting the reports right first time, as soon as the week ends. Many companies have integrated their digital systems (based on ERP such as SAP, Oracle, Tally, Zoho) and provide simple excel based reports and dashboards which can be accessed across devices such as mobile phones or tablets.

During the last two years of the pandemic, there have been lot of uncertainties in supply chain. Moving to a digitally enabled model has allowed these companies to be extremely nimble and agile in their decision making. Several companies have pivoted their business model quickly in order to capitalize on the emerging opportunities in the market. These decisions have been backed by analysis of marketing trends using simple AI and ML based algorithms, dynamic decision making matrix and partnerships across the digital ecosystem.

Technology acceleration has also helped some companies to take specific actions to address business challenges posed by the pandemic. For e.g. to deal with the disruption in logistics, companies have invested in GPS based end to end tracking systems. In manufacturing businesses, use of IOT based sensors has picked up significantly to collect data, and share timely alerts for predictive maintenance.

At Goldratt India, we have been working with Indian companies for over 23 years to help them increase their sales, profit and cash flow by an order of magnitude. Weekly reviews have been the cornerstone of all our engagements. Companies have been able to achieve quantum improvement in performance, just by changing a few metrics and review processes. Some of our learnings are  encapsulated below:

  1. Measure performance weekly instead of monthly
  2. Don’t get stuck in analysis paralysis, focus on corrective actions only
  3. Instead of chasing benchmarks or budgets, always strive to “Better than Before” with respect to own past performance
  4. Monitor plan vs. actual every week: The more our planning improves, the gap between plan vs. actual reduces
  5. Better than Before: Each week, strive to improve upon past 13 week moving average, irrespective of the external environment

Our client JSPL has been practicing these principles for over 5 years and is well on its way to becoming a debt free company. The company has reduced debt by over Rs 25000 crores in the last 4 years.

Short video from the case study presented at TOCICO international conference in USA:

In conversation with Mr. Naveen Jindal, Chairman, JSPL