Like many industries, the brewing industry embraced AI to improve marketing and supply chain functions. They used real-time data collection on people's reactions to creatives to adjust them for maximum impact. Distributors were also treated using machine learning, which could identify optimal times and amounts to send distributors as well as identify risks and determine suitable credit terms.
However, efforts are underway to incorporate AI technology into one of the industry's core processes: beer brewing. AI is being employed by manufacturers across various industries in various forms; some AI products have already hit the market, while others remain in the development stages.
AI Can Determine The Recipe
Brewers have long experimented with beer recipes, and AI can help them incorporate customer feedback more efficiently. Some companies choose to compare recipes from the ten best and worst beers on the market using an algorithm to identify an ideal recipe; other companies collect consumer data. IntelligentX asked consumers for feedback on four types of beer they liked. Over 1,000,000 data points were ordered to find their preferred recipe for making beer.
AI Serves Beer:
AI was used to develop a robot capable of serving beer accurately. Participants watched videos showing this robot offering various colors and levels of foam, and data was gathered about what participants preferred from each combination. Now, this robot can standardize customer experiences while maintaining consistent foam levels across every drink served by this brewery.
AI Brew Beer:
AI can help predict beer quality by monitoring CO2 levels during brewing in real time and collecting data at various stages. Many companies also attempt to use data collection for fermentation prediction; real-time analyses save time and effort while improving accuracy - keeping the industry energy, money, and time. Ab InBev has extensively researched these lines.
Brewing beer requires art and science, so using AI to enhance the process can be a great way to add precision while satisfying both aspects. Brewers also gain more freedom when creating their beer recipes through experimentation.
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Artificial Intelligence: Four types
Arend Hitze is an associate professor in Integrative Biology and Computer Science and Engineering departments. Recently published an article in which he classified artificial intelligence (AI) into four main types. These included:
Reactive Machines
These artificial intelligence (AI) machines can only react in response to their surroundings; they do not utilize experience to guide current or future decisions; rather, they perceive and act on what they see directly. Reactive machines tend to perform only limited, specific tasks; therefore, they tend to be more reliable and trustworthy yet always respond identically to stimuli.
Reactive Machines Include
A Google subsidiary is an artificially intelligent computer game capable of playing Go and representing its first complete system to defeat professional Go players and world champions. Weiqi (pronounced Wee-qu) is an abstract board game in which two opponents compete to surround more territory than their opponent. Originating in China over three millennia ago, Weiqi may be one of the oldest continuing board games today.
Artificial intelligence tools were made more difficult by the complexity of this game. Even powerful computer programs would find it challenging to compete at human-like levels when faced with 170 configurations on a board that contains 170,000 possible arrangements - ten times more atoms than known!
Combines deep neural networks and search trees. It takes the description of a board and processes it through several layers of a network consisting of millions of neuron-like connections to process the board description.
A neural network predicts its winner based on its value network, or "value network." After being introduced to various amateur games and playing against itself countless times, it learned from any mistakes it made along the way and, , won against a professional Go player with ease, 5-0! was introduced as a self-taught system that has quickly mastered Go, chess, and shogi, beating world champion programs in all three games. This version replaces the hand-crafted heuristics of previous versions with algorithms and a neural network, creating a unique style of play.
MuZero is the newest version that utilizes an environment model and lookahead search to learn Atari games with complex visual elements, such as Go, Chess, and Shogi, without needing to be taught the rules or strategy - making him ideal for planning winning strategies for unknown domains - something essential when developing future general-purpose learning software solutions.
Deep Blue initially lost to Garry Kasparov 4-2; however, in 1997, after upgrading, Deep Blue was victorious in two of six games against him, winning three, drawing one, and then finally taking control for good in game six. The impact was felt across many industries; not only was its original intention to solve complex chess puzzles, but its use by researchers also provided them with insight into and explored the limits of massively parallel processing.
Deep Blue's architecture has been used successfully in molecular dynamics (helpful in discovering and developing new drugs), financial modeling (such as market trends and risk analyses), and data mining applications.
The AI-powered system, Watson, was inspired by Deep Blue and beat two top human Jeopardy players. Equipped with software capable of processing and reasoning about natural language processes, massive amounts of information received before competing were utilized; this proved that human-machine interaction could become even more advanced.
Memory Limits
Memory machines with limited memory can only look backwards when gathering information. As they collect it, however, these memory machines store past data and make predictions based on it. Reactive machines differ significantly in complexity from these more sophisticated memory ones because their creation involves continually training a model to analyze and utilize data.
Three Main Machine-Learning Models Exist For Al Machines With Limited Memory Capacity
Evolvable Generative Adversarial Networks. These evolve by exploring various paths with each new decision made, becoming more advanced with every decision taken. To predict its evolutionary mutation cycle and predict outcomes more accurately. This machine-learning model uses simulations and statistics.
Long Short-Term Memory. Long Short-Term Memory users rely on past events to predict what will come next in a series, prioritizing more recent data as crucial when making their predictions and disregarding older information but using it nonetheless to conclude.
Reinforcement Learning. These models employ repeated trial and error to make more accurate predictions.
Self-driving vehicles, for instance, can monitor the direction and speed of cars by identifying and tracking them over time. By using this data in combination with pre-programmed representations such as traffic lights or curves in the road, self-driving vehicles can decide when to switch lanes based on this information. Short-term data storage does not occur within a limited memory machine's library of experiences.
The Theory Of Mind
Artificial intelligence apps will take their next steps by adopting the psychological concept of "theory of mind." Machines utilizing this system will represent humans, animals, and objects with emotions and thoughts that affect their behaviors.
This feat will also allow machines to understand the feelings and motivations of humans, animals, and other devices and make decisions based on self-reflection and determination based on data they collected themselves. Understanding concepts such as "mind," emotion, and others will be vital in real-time.
Self-Awareness
AI scientists must study consciousness to recreate it so machines are aware of themselves. The first steps in developing AI, such as what we see today in movies and games, include understanding learning, memory retention, and making decisions based on previous experiences. Detroit: Become Human is a game that explores these concepts of artificial intelligence as it develops, exploring whether these machines should have human rights.
Read more: 7 Types Of Artificial Intelligence (AI)
Artificial Intelligence Applications
Computer Vision
Computer vision involves developing advanced techniques to enable computers to understand the information contained within digital images, videos, and other forms of inputs, such as input from radiology machines or eCommerce transactions. Complex neural networks for computer vision have many applications across radiology, eCommerce, and other fields.
Speech Recognition
Automatic Speech Recognition (ASR) uses natural language processing techniques to convert human speech into text. Google Assistant and other digital assistants utilize machine learning and natural language processing to comprehend human speech; they can understand complex commands with satisfactory outputs. Digital assistants have evolved beyond simply answering queries; they now can analyze user preferences, schedules, and habits while organizing reminders and schedules.
Recommendation Engines
AI algorithms use past consumer data to predict trends to provide more effective cross-selling techniques, providing relevant recommendations during checkout processes for online stores. Spotify, Netflix, and YouTube are media streaming platforms that employ AI-powered intelligent recommendation systems to collect user data about their interests and behavior, analyze it with machine learning algorithms, and predict their preferences using machine learning and deep-learning methods.
Customer Service
Chatbots have quickly become an alternative way for businesses to engage with customers on social media platforms as well as websites by answering FAQs (frequently asked questions), providing personalized advice, and cross-selling products and services. Messaging bots have transformed how businesses connect with their target market via the internet.
Chatbots
Chatbots, or "chat robots," commonly known, are used in customer service to simulate human agents by mimicking their conversational style with Natural Language Processing (NLP). Chatbots can answer specific inquiries while learning from negative reviews as they go.
Face Recognition Technology
Apple's TrueDepth Camera projects over 30,000 invisible points onto the user's face to create an infrared picture and depth map, then compares this scan against previously enrolled data to determine if it can unlock their device.
Social Media
Platforms like Instagram and Facebook use artificial intelligence (AI) to customize what users see in their feeds by recognizing users' interests and recommending similar content that keeps them engaged with what's being shown. AI models can also be trained to recognize specific keywords, symbols, or phrases from multiple languages to block out hate speech content. Social media also uses AI through emojis for predictive text, filters to remove spam, facial recognition technology to automatically tag friends in photos, and smart replies.
Text Editor
Document editors use Natural Language Processing to detect and correct grammar errors. At the same time, readability and plagiarism detection technologies are sometimes included as well. Advanced tools provide intelligent recommendations for optimizing web content to drive more website traffic.
Search engine
Google's search algorithms offer top results in SERP with answers relevant to user queries. In contrast, quality control algorithms help identify content of high quality and ensure search results that provide an excellent user experience. In addition, these search engines employ natural language processing technology to interpret queries more accurately.
Smart Home Devices
Smart thermostats use artificial intelligence (AI) applications that understand your daily habits and preferences regarding cooling and heating needs. In contrast, smart fridges create shopping lists based on any gaps they detect on the shelves.
How Does Artificial Intelligence Work?
Artificial intelligence (AI) is created through the combination of specialized computer software processing power with large datasets. Once trained on these datasets, artificial intelligence can spot patterns and make inferences about any questions asked. We now consider artificial intelligence a computer program that uses superhuman pattern recognition capabilities and high processing power to solve specific problems.
There are various methods of categorizing artificial intelligence, with Arend Hintze, a fake intelligence professor, often mentioned. His categorization system divides AIs into four categories depending on their awareness of their environment and other attributes.
- Reactive Machines: Artificial intelligences that respond to stimuli. These machines can detect and react accordingly when events arise.
- Limited Memory: Limited memory artificial intelligence should be considered more as bots that perform.
- Specific Tasks: think chatbots and autonomous cars, for example.
- Theory: AIs understand other things and have minds.
- Self-Aware AIs: An artificial intelligence capable of self-awareness may even perceive itself as conscious.
How Do You Create Artificial Intelligence?
As the initial step to developing artificial intelligence (AI), identifying your problem is critical as computers have yet to reach the power necessary for creating AI capable of solving every challenge. Once you've identified your goal, the next step should be collecting relevant data to your problem before "training" an Artificial Intelligence algorithm you have devised or is available from third parties.
After training the AI to comprehend data sets, its final step involves providing it with the environment necessary to perform its duties - which may include installing hardware or finding server space for software.
Where Did Artificial Intelligence Originate?
Alan Turing was among the earliest to clearly articulate it. In his 1950 article entitled Computers and Intelligence, Turing raised questions about whether machines could think and how their intelligence might be assessed. Also introduced in this paper was what is now known as the Turing Test, which measures whether machines possess intelligence.
In 1956, the Dartmouth Summer Research Project on Artificial Intelligence in Dartmouth (New Hampshire) initiated the modern study of AI. At this eight-week conference, nearly fifty scientists, including Marvin Minsky and Claude Shannon, debated how to create machines capable of thinking for themselves.
Artificial Intelligence would take decades to fully develop. However, discussions held at this lengthy conference had an immense effect on shaping its concepts and ideas into the foundations of artificial intelligence.
Why Is Artificial Intelligence Important?
Successful AI has many advantages. Artificial programs are much better at recognizing small details and patterns than humans, making them perfect for certain technology applications. AIs also assist humans with daunting tasks, such as solving mathematical equations and performing repetitive duties.
AI technology is becoming a very attractive option for businesses. AI plays a key role in their business processes and allows workers to focus more efficiently. AI also gives humans the power they need to live better lives.
Artificial Intelligence And The Real World
Many companies and organizations have adopted AI-like technology, regardless of whether or not you believe AI already exists today.
AI Imaging In Healthcare
Artificial intelligence software is used in healthcare settings to assist doctors and technicians in identifying abnormalities such as cancerous growths or tumors. Artificial intelligence can also be employed by MRI scanners to convert scans to high-quality images that help doctors make a quicker diagnosis because they provide a clearer view of what's happening inside our bodies. Oren, Gersh, and Bhatt have published a journal article discussing how software could be enhanced by providing instructions regarding its scanning process.
Self-Driving cars
Self-driving vehicles are among the most recognized applications of AI in real life, using cameras and GPS to detect obstructions and combine that information with artificially intelligent software that drives without human input.
Facial Recognition
AI's use in law enforcement is highly contentious. Sophisticated programs use AI to analyze photos of people's faces and link them with other images. Artificial intelligence software enables fast matching of faces using facial features and shapes - something humans cannot achieve independently.
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Conclusion
As we have seen in two examples of programming software used in the beer industry, artificial intelligence (AI) can greatly benefit businesses and customers. Companies can better meet customers' expectations while improved products help customers - all thanks to AI! These examples demonstrate how AI can be utilized across various beer production techniques.
