Can AI reduce the world’s food waste?

Here is a shocking statement: “We waste 1.6 billion tons of food every year while 25 million starve and another billion are malnourished.” It should make us all pause to consider.

However, as John Koetsier writes, there is a Berlin startup with a possible AI solution to the complex problem of food waste.

It is a complicated situation because of the vast numbers of farmers involved in the global supply chain. As Koetsier says, “Tens of millions of farms feed millions of grocery stores and restaurants, which in turn supply almost eight billion people their daily food.” Add to that the transport companies, wholesalers, distributors, processors, and delivery companies and you have a massive web that needs to communicate effectively, and it has to do its best to preserve perishable products. And that is what startup company, is trying to fix.

The company was among eight winners of the Extreme Tech Challenge. Some 2,400 entrants sought to deliver solutions to global challenges and SPRK got the judge’s approval. Its goal is to “use AI to understand the flow of food and reduce waste.” Its theory is that this should stop over-production of food and reduce hunger at the same time.

SPRK’s CEOAlexander Piutti told Koetsier: ““Half of the food that gets produced gets wasted sooner or later. Once you move into understanding patterns — why there are food waste cases — you understand these patterns and see they come in a regular fashion … we can move from reactive to proactive, to anticipating, to predicting with a certain probability.”

Food waste is also an environmental problem. Overproduction uses resources like fuel, water, fertilizer and it increases greenhouse gas emissions, because when you waste food it goes to landfill and it emits more CO2.

How can AI fix food waste?

First it has to understand the supply chains and food economics. Oversupply is likely to be given to NGOs and food banks rather than competitors, and SPRK has to take account of this in the rules for its AI system. “Once we have these rules, we can inject them into the technology,” Piutti says. “The technology takes over … and matching between oversupply and demand … becomes more intelligent over time.”

It is starting with the foodbanks, which are typically low-tech. SPRK is building software for food banks that they can use to manage their own operations as well as collaborate with others, sharing being the key aim here. Piutti also says the software will give food banks better ways to access food at lower prices.

He said of the food banks: “They purchase food in a very normal fashion, they don’t get discounts. If we can connect the dots conceptually and say like, well, what if we distributed this food oversupply to the folks in need … they become a volume partner.” He says AI software can manage all this and save NGOs around 50% of the money they spend and reduce food waste at the same time.

As for SPRK, its CEO said, “Our vision is a world without food waste where everyone — including future generations — have enough to eat and thrive.” It’s an admirable goal!

Where AI Gives a Competitive Edge

Use of Artificial Intelligence (AI) is growing. According to International Data Corporation (IDC) forecast statistics, spending on the technology will reach $97.9 billion in 2023, which is two and a half times what it was in 2019.

David Schubmahl, IDC research director, said, “The AI market continues to grow at a steady rate in 2019 and we expect this momentum to carry forward,” adding, “The use of artificial intelligence and machine learning (ML) is occurring in a wide range of solutions and applications from ERP and manufacturing software to content management, collaboration, and user productivity. Artificial intelligence and machine learning are top of mind for most organizations today, and IDC expects that AI will be the disrupting influence changing entire industries over the next decade.”

Who is leading AI spending?

According to IDC: “Spending on AI systems will be led by the retail and banking industries, each of which will invest more than $5 billion in 2019. Nearly half of the retail spending will go toward automated customer service agents and expert shopping advisors & product recommendation systems. The banking industry will focus its investments on automated threat intelligence and prevention systems and fraud analysis and investigation. Other industries that will make significant investments in AI systems throughout the forecast include discrete manufacturing, process manufacturing, healthcare, and professional services. The fastest spending growth will come from the media industry and federal/central governments with five-year CAGRs of 33.7% and 33.6% respectively.”

Marianne D’Aquila, IDC research manager in the Customer Insights & Analysis department said, “”Artificial Intelligence (AI) has moved well beyond prototyping and into the phase of execution and implementation.” She also remarked, “Strategic decision makers across all industries are now grappling with the question of how to effectively proceed with their AI journey. Some have been more successful than others, as evidenced by banking, retail, manufacturing, healthcare, and professional services firms making up more than half of the AI spend. Despite the learning curve, IDC sees higher than average five-year annual compounded growth in government, media, telecommunications, and personal and consumer services.”

The biggest AI use cases

The three largest use cases for AI currently are: automated customer service agents, automated threat intelligence and prevention systems, and sales process recommendation and automation. But in the future, the use cases that will see the fastest spending growth over the 2018-2023 forecast period are automated human resources (43.3% CAGR) and pharmaceutical research and development (36.7% CAGR). However, eight other use cases will have spending growth with five-year CAGRs greater than 30%.

Where is AI investment happening?

According to IDC, the United States will deliver more than 50% of all AI spending throughout the forecast, led by the retail and banking industries. In second place is Western Europe where banking and discrete manufacturing will show the biggest demand. China will be the third largest region for AI spending with retail, state/local government, and professional services being the leading users, and Japan is another country to watch as its AI use is predicted to have a 45.3% CAGR growth in spending on AI up to 2023.

AI Speeds up Drug Design as World Searches for Covid-19 Cure

The use of Artificial Intelligence (AI) is surging, and one area in which its use is even more important in 2020 – The Year of a Global Pandemic –, is in health technology, or healthtech.

Berlin-based, non-profit think tank dGen has just released a new report, AI, Privacy, and Genomics: The Next Era of Drug Design”, which looks at the issue of privacy and access to genetic data for companies using AI to speed up and improve drug design.

With articles about work on a vaccine for Covid-19 flooding newspapers every day, most people are now more aware of the typical timelines involved in creating new drugs. They certainly don’t appear overnight: as the dGen report states, “the average drug today takes 10-12 years and cost $2 billion.” However, the arrival of the new coronavirus has forced researchers to think more in terms of 12-18 months, which is like exchanging a leisurely world cruise for supersonic flight.

Enter AI, which in 2020 is only just entering drug R&D where it is being used to test and improve candidate drugs before they can be fully accredited by regulators, such as the FDA in the USA.

But there is one area of concern: genetic information is central to many AI-enabled drug discovery startups. To expand this innovation, a number of issues with using genetic data must be resolved. The dGen report lists these as:

  • ownership
  • secure storage
  • availability to multiple research parties.

In order for all of us to accept the wider use of AI in drug design, privacy concerns must be addressed using the privacy-preserving techniques in Machine Learning. These allow researchers to process genetic material without fully revealing its source.

While this is crucial for privacy, the techniques do not address the issue of ownership, or ways to audit the system. What the dGen report proposes is a decentralised pan-European biobank network that makes information available to researchers, but all access requests have to be logged. In that way, we all know who has looked at our genetic data. Furthermore, it would allow us as individuals to grant or deny these requests and track the use of our information.

dGen’s Top Predictions for 2030

Based on better privacy-preserving technologies and an access network, dGen’s top predictions for 2030 are:

  • Better collaboration networks will emerge.
  • Genetic privacy laws will be overhauled.
  • AI will become a fundamental part of drug discovery.
  • Pharmaceutical giants won’t be toppled, but they won’t get out unscathed as biotech startups take the field.

The views of industry leaders

It is also interesting to note the responses to the dGen report from those working in AI and healthtech. The think tank interviewed industry leaders from Aidence, Gero, Alphanosos, e-Estonia, Qunatlib and Turbine amongst others.

Maxim Kholin, Gero Co-Founder said,‘We believe that AI can accelerate the drug discovery process by proper understanding of human diseases from large biomedical data. The data-driven approach should help establish the genetic determinants and molecular markers of the disease’. While, Pascal Mayer, Founder of Alphanosos, which is specialised in plant-based pharamaceuticals, told dGen: ‘While currently working really well on bacteria, we are confident AI-enabled plant-based drug discovery shall be successful in fighting viruses as well’.

This work is important for all of us who have an interest in how emerging technology can contribute to a decentralised future in Europe and what this might mean for people, society, private entities, and the public sector over the coming decades. It is, of course, of a signifiant interest to us all right now, as the search for a Covid-19 cure continues.

For a copy of the Full Report contact:

Francisco Rodríguez

7 Trends of the 4th Industrial Revolution?

Things are moving fast in our world, with technology leading the transformation of businesses, job and society generally. The next decade is going to define the latest Industrial Revolution and there are a number of technology trends that are playing a core role.

Artificial Intelligence

Artificial intelligence (AI) and machine learning refer to the ability of machines to learn and act intelligently. We are already using it at home as Amazon presents us with products we might be interested in based on previous purchases. But it is going to get even bigger, and we will see it carry out a wide range of human-like processes, such as seeing (facial recognition), writing (chatbots), and speaking (Alexa).

The Internet of Things

This refers to everyday devices and objects that are connected to the Internet and which gather and transmit data. We have smartphones already, but soon we will have smart fridges, and smart everything.

Big Data

This is all about the explosion in the amount of data that is being generated as more ‘thing’s and services are digital. By analysing masses of data with intelligent algorithms, companies can identify patterns and relationships that they couldn’t see before, allowing them to offer more personalised services.


Although blockchain has been around since 2009, it is still expanding and changing its uses beyond cryptocurrency. Expect to see blockchain being used for storing, authenticating, and protecting data, and transforming banking.


Robots are intelligent machines that can understand and respond to their environment and perform routine or complex tasks by themselves.

We will see more Cobots in the next few years. These enhance the work that humans do and interact safely and easily with the human workforce. They are your new work colleagues!

5G Networks

5G is the fifth generation of cellular network technology, and it will deliver much faster and more stable wireless networking. It is necessary for all the ‘smart’ things we’re going to have, as mentioned above.

Quantum Computing

Quantum computing will make our current systems look as though Fred Flintstone used them. It will completely redefine what a computer is, and is bound to be a game changer in the world of AI.