Many people still associate AI with science-fiction dystopias, but that characterization is waning as AI develops and becomes more commonplace in our daily lives. Today, artificial intelligence is a household name – and sometimes even a household presence (hi, Alexa!).
While acceptance of AI in mainstream society is a new phenomenon, it is not a new concept. The modern field of AI came into existence in 1956, but it took decades of work to make significant progress toward developing an AI system and making it a technological reality. In business, artificial intelligence has a wide range of uses. In fact, most of us interact with AI in some form or another on a daily basis. From the mundane to the breath-taking, artificial intelligence is already disrupting virtually every business process in every industry. As AI technologies proliferate, they are becoming imperative to maintain a competitive edge.
“Artificial intelligence” is a broad term that refers to any type of computer software that engages in humanlike activities – including learning, planning and problem-solving. Calling specific applications “artificial intelligence” is like calling a car a “vehicle” – it’s technically correct, but it doesn’t cover any of the specifics. Machine learning is one of the most common types of AI in development for business purposes today. Machine learning is primarily used to process large amounts of data quickly. These types of AIs are algorithms that appear to “learn” over time. If you feed a machine-learning algorithm more data its modelling should improve. Machine learning is useful for putting vast troves of data – increasingly captured by connected devices and the Internet of Things – into a digestible context for humans.
Rather than serving as a replacement for human intelligence and ingenuity, artificial intelligence is generally seen as a supporting tool. Although AI currently has a difficult time completing common sense tasks in the real world, it is adept at processing and analysing troves of data much faster than a human brain could. Artificial intelligence software can then return with synthesized courses of action and present them to the human user. In this way, we can use AI to help game out possible consequences of each action and streamline the decision-making process.
Food and Packaging Sector
We’ve seen many innovations related to the service, production, packaging and distribution of food over the years. These changes all aim to protect public health and create a more efficient and sustainable food industry. You may have heard about the use of AI in automated scanning for physical defects, but have you heard about it being used in detecting other sensory cues such as smell and taste? The systems of AI have been integrated into machines that can sense odours and flavours intuitively for the control of food safety and quality as well as deep analysis of product composition. In such innovations, features used in the extraction and recognition of patterns are used to come up with resulting details. Sensors are used to extract the marker components, and intelligent algorithms translate the elicited signals to come up with the information needed.
This innovation removes the need for food handlers to conduct the sensory analysis themselves, which can negatively affect their health. In addition, the use of AI in the selection and analysis of food components and characteristics — as well as in the detection of defects — ensures a more uniform approach while reducing the possibility of errors.
On a more business-centric point, AI Innovations have also significantly contributed to improving food safety management. Food companies have adapted to the shift in focus of food agencies toward proactively addressing food-safety hazards. Businesses have started to use automated monitoring procedures and have become less dependent purely on human resources.
Artificial intelligence has propelled the food industry to greater lengths. With less human intervention, the percent damages and errors from unpredictable human behaviour are minimized. The advances in terms of service, preparation and management of food operations makes life even easier for food business owners, operators and consumers alike. The future is looking even more promising as food companies and technology innovators strive to satisfy consumers while keeping them healthy in the most efficient way possible.
Packaging and Waste Management
Many governments and non-governmental organizations (NGOs) alike agree that single-use plastics account for a majority share in issues related to environmental stewardship. Ocean debris, damaged marine life, and congested landfills are all signs of a plastic management issue. While sustainable packaging is expected to stay at the top of consumers’ concerns in 2022, debates around plastic reusability models and the future of throwaway plastics are likely to heat up.
As of 2022, a quarter of worldwide plastic waste is incinerated while 40% is disposed of in landfills. To address this issue, packaging companies are introducing intelligent packaging that tackles plastic waste management, sorting, and collection issues, promotes circularity, and reduces the carbon footprint of their packaging. According to Future Market Insights’ (FMI) report, ‘Intelligent Packaging Market’, demand for intelligent packaging will reach US$46.7 billion over the next decade.
Packaging behemoths are anticipating the use of IoT, blockchain, and AI to improve plastic waste collection, logistical efficiency, data analytics, and customer experience. Given that containers and packaging account for a significant component of municipal solid waste (MSW), accounting for 28.1% of total generation, sustainable packaging remains a highly profitable possibility.
A huge amount of today’s plastic garbage is thrown into the oceans, wreaking havoc on aquatic flora and animals. Plastic ocean debris has reached alarming proportions and must be handled as quickly as possible.
AI robots might be quite useful in our efforts to clean up the ocean. AI-powered robots can be deployed to scour for plastic debris that has accumulated on the water’s top or rubbish that has accumulated at the ocean’s bottom. These robots can be deployed in swarms, making the procedure both more productive and cost-effective. The AI system can also be taught to recognize and discriminate plastic garbage so that it may be collected for recycling without damaging marine biodiversity.
AI-powered robots equipped with computer vision technology may be taught to sort plastic garbage by modifying their algorithm. When compared with people, these robots can work quicker and longer, and they can sort plastic garbage more efficiently. AI powered robot has the potential to save between USD$120,000 and USD$130,000 per year. Today, out of every US$1,000 spent on packaging, around US$20 is spent on intelligent packaging and these numbers are expected to grow quickly. The global market for intelligent and sustainable packaging is experiencing steady growth driven by gradual but robust commercialization, with intelligent packaging expected to account for 5% of total packaging by 2030.
Artificial Intelligence in the Beauty and Cosmetics Market
The beauty and cosmetic sector have witnessed a massive upsurge in Artificial Intelligence (A.I.) in recent years. Due to advancements in A.I. technologies and the fact that beauty is characterized as a personalized and engaging market that generates a large amount of data, A.I. appears to be a solution to deal with this complex environment, prompting beauty companies to make data-driven decisions on their strategies to remain competitive.
The beauty market has changed dramatically over the last decade, owing to the introduction of new technology and a shift in customer shopping behaviours. The beauty sector has been incorporating digital transformation into its business models to give consumers individualized skin regimens and beauty products tailored to their specific needs.
The Integration of advanced technology like A.I. in the beauty and cosmetic field provides new ways of engaging with the consumer, bringing efficiency and customised solutions to the beauty client such as virtual try-on and personalized products. Increased demand for beauty products and technological advancements is expected to positively impact market growth.
There has been an influx of Beauty Tech implementations on the global market with the rapid expansion of the beauty and cosmetic industry. Key companies are constantly testing and launching new features with key strategic partners with innovative services, covering the market’s demands. Their focus on serving their clients’ needs, both brands and end-consumers, and the constant technological development are the key factors in boosting market growth. Companies like L’Oréal, and PROVEN, among others, have already recognized such potential and are applying A.I. in different ways. For instance, L’Oréal is implementing A.I. strategies on their business.
Environmental Sustainability
Ericsson is a great example of companies using A.I. to be environmentally conscious. Proactive management of topics relating to climate action and the environment is a core component of Ericsson’s sustainability strategy. makes up one of three main sustainability focus areas
With the growing threat of global warming, the negative impact of carbon emissions is an urgent worldwide concern. Pressure on businesses to accelerate climate action and limit global warming has never been more prevalent and the corporate world is making commitments for delivering its ambition to become Net Zero across its value chain. Transportation alarmingly accounts for 29 percent of global greenhouse gas (GHG) emissions.
Ericsson takes the need to decarbonize seriously. To address the threat head on, Ericsson has committed to reach Net Zero emissions in their value chain by 2040. Ericsson is already working towards a first major milestone to cut emissions by 50 percent in the supply chain and portfolio by 2030 and become Net Zero in their own activities at the same time. One of the key activities to address the reduction of supply chain emissions is Product Transportation.
The primary challenge in developing a solution was the lack of availability of data, related data from various sources and developing the precise logic to calculate the CO2 equivalent emission. With the help of various analytical techniques and fuel-based, distance-based, cost-based methodologies, Ericsson was able to calculate the emissions associated with transportation. After multiple trials and errors, the distance-based method was found to be the best suited approach for Ericsson Transport Management. They derived the CO2 equivalent emission by modelling frequent parameters like the volume of goods purchased, distance travelled, the standard emission factor for respective transport mode and/or type, and so on. The model was built generically enough to fit most similar transportation services.
AI is already shaping up to be the key to empowering governments, organizations, and individuals to make more conscious decisions and work towards creating a healthier planet. The gravity of climate change together with artificial intelligence’s potential makes it too essential not to try\
Health Care
Machine learning is a rapidly rising technology with exciting implications for healthcare. Already it’s helping tackle some of the most difficult issues in the space, from making sense of huge volumes of patient data to improving the quality and personalisation of treatment and care. Machine learning is one type of technology within the cluster of technologies known as artificial intelligence. According to one definition of machine learning, it’s a statistical technique for applying models to data and having AI learn by training these models with data.
Some of the biggest machine learning trends in healthcare to be aware of are:
- Precision medicine and personalisation of healthcare
- Analysing imaging – Machine learning is already used to analyse radiology and pathology images
- Prediction and health policy – Machine learning offers immense potential for predictive modelling and health policy.
- Electronic health records
- Diagnosis and treatment – Machine learning is increasingly being used for diagnosis and treatment recommendations.
- Drug development – Researchers rely on machine learning to put together cohorts for expensive clinical trials
The world is now provided with a big opportunity to leverage the potential of artificial intelligence to generate more tailored and individualized client experiences while maintaining sustainability. If harnessed correctly, it can change the world.
