AI Flashcards
(20 cards)
How is AI reshaping the automotive industry?
Automotive manufacturing requires precision and accuracy, and AI can help enhance that.
What is the automative AI industry projected to hit at 2027?
The automotive AI market is projected to hit $7 billion by 2027, highlighting it as one of the leading industries in adopting AI in manufacturing.
How is AI reshaping the electronics industry?
Electronic manufacturing also requires precision due to its intricate components, and AI can be critical in minimizing production errors, improving product design and accelerating time-to-market.
What are Ford and BMW doing with AI?
Ford employs cobots for welding, gluing and quality control tasks. It uses six cobots to sand the entire body surface of a car in 35 seconds.
BMW’s Spartanburg plant, producing 60% of U.S. BMWs, uses AI-managed robots, saving $1 million yearly and reallocating workers.
What is Samsung’s South Korea Plant doing with AI?
Using automated vehicles (AGVs), robots and mechanical arms for tasks like assembly, material transport, and quality checks for phones like Galaxy S23 and Z Flip 5. These tools can help companies maintain high-quality standards, including inspections of 30,000 to 50,000 components.
What is Nvidia doing with AI?
They are using AI to optimize the placement of intricate transistor configurations on silicon substrates, which not only saves time but offers greater control over price and speed. It proved its efficiency by optimizing a design featuring 2.7 million cells and 320 macros in just three hours.
How is AI reshaping the Aerospace and defense industry?
AI-driven manufacturing enhances product safety and reliability by producing precise components, boosting performance and system safety.
How much was AI in the aviation market worth in 2022? What percentage CAGR ( compound annual growth rate- the annual growth of your investments over a specific period of time. ) is it expected to have?
686.4 million dollars
20%
What is Airbus doing with AI?
Airbus, with Neural Concept’s tech, cut aircraft aerodynamics prediction time from one hour to 30 milliseconds using ML. This kind of productivity boost can enable design teams to explore 10,000 more changes in the same time frame as the traditional computer-aided engineering approach.
What is Rolls-Royce doing with AI?
Rolls-Royce uses AI in aerospace manufacturing through the Blue Data Thread strategy. This approach utilizes digital twins and AI for predictive maintenance, resulting in a 48% increase in time before the first engine removal.
How is AI reshaping the food and beverage industry?
Food and beverage production requires advanced quality assurance, particularly in the fast-moving consumer goods (FMCG) sector, due to its “high-speed” nature. Equipment breakdowns and faulty products can hinder that; however, integrating AI can boost efficiency, cost-effectiveness and product quality and safety.
What sum is the global AI market for the food and beverage industry expected to reach in 2028?
35.42 billion dollars
What is food and beverage start-up Augury Inc doing with AI?
Startups specializing in predictive maintenance technology are particularly in demand. Take Augury Inc., for instance. They helped PepsiCo’s Frito-Lay gain 4,000 hours of manufacturing capacity annually through its predictive maintenance systems that decreased unplanned downtime and costs at four Frito-Lay plants.
How is AI reshaping the Pharmaceutical industry?
It usually takes a decade to develop a drug, plus two more years for it to reach the market. Unfortunately, 90% of drugs fail in the clinical trial phases, resetting the clock. AI can accelerate drug development and enhance quality control.
How is pharmaceutical company Pfizer using AI?
Pfizer, using IBM’s supercomputing and AI, designed the Covid-19 drug Paxlovid in four months, reducing computational time by 80% to 90%.
What could AI do for drug development? How many companies are advancing AI-driven drug development?
It could generate 50 new drugs, resulting in $50 billion in sales over a decade.
Over 80 companies are advancing AI-driven drug development, attracting investments from pharmaceutical giants.
What are the three areas where AI can alleviate drug-discovery challenges?
- Protein Structure Prediction: AI systems like AlphaFold2 have transformed protein structure prediction, enabling researchers to understand the blueprints of complex molecules accurately and potentially saving years of lab work.
- Function Forecasting: A recent Forbes article explored how AI models can predict how large molecules function and understand how proteins bind to their targets and the movement of antibodies, enhancing the development of therapeutic responses.
- New Therapies Design: AI algorithms use vast data to design proteins, antibodies and mRNA structures for diseases like cancer. Genesis Therapeutics, for example, uses AI to design and predict the effectiveness, specificity and potential side effects of new medication.
What did a recent survey conducted by Augury of 500 firms reveal?
63% plan to boost AI spending in manufacturing.
What is marketsandmarkets financial projection for AI in the manufacturing market by 2028?
20.8 billion
What are the three pillars that AI in manufacturing rely on and what are the five steps to develop all three pillars
Three pillars: problem, people and process.
- Define the problem: Identify inaccuracies causing higher costs. New-to-AI companies should break down the issue into SMART goals and assess AI’s potential for long-term cost savings.
- Address resources and data: Form a diverse team of technical and business experts. Evaluate in-house capabilities and consider outsourcing or hiring. Verify data sufficiency, clean and structure the data and decide on storage solutions.
- Assess data quality: Is the data modern, accessible and sufficient? Modify as needed.
- AI model considerations: Decide to build, buy off the shelf or adopt a hybrid approach.
- Fine-tuning and deployment: Discuss model refinement, deployment and scalability. Adhere to ethical guidelines and frameworks in each step- crucial to check for bias.
