Control AI and IndustryGPT: Whatever has Been Done, Can be Outdone
Capturing this week's zeitgeist
Elon Musk, Steve Wozniak, and more have asked for a “Pause Giant on AI Experiments: An Open Letter.”
AI systems with human-competitive intelligence can pose profound risks to society and humanity, as shown by extensive research and acknowledged by top AI labs. As stated in the widely-endorsed Asilomar AI Principles, Advanced AI could represent a profound change in the history of life on Earth, and should be planned for and managed with commensurate care and resources. Unfortunately, this level of planning and management is not happening, even though recent months have seen AI labs locked in an out-of-control race to develop and deploy ever more powerful digital minds that no one – not even their creators – can understand, predict, or reliably control.
Contemporary AI systems are now becoming human-competitive at general tasks, and we must ask ourselves: Should we let machines flood our information channels with propaganda and untruth? Should we automate away all the jobs, including the fulfilling ones? Should we develop nonhuman minds that might eventually outnumber, outsmart, obsolete and replace us? Should we risk loss of control of our civilization? Such decisions must not be delegated to unelected tech leaders. Powerful AI systems should be developed only once we are confident that their effects will be positive and their risks will be manageable. This confidence must be well justified and increase with the magnitude of a system’s potential effects. OpenAI’s recent statement regarding artificial general intelligence, states that “At some point, it may be important to get independent review before starting to train future systems, and for the most advanced efforts to agree to limit the rate of growth of compute used for creating new models.” We agree. That point is now.
Therefore, we call on all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4. This pause should be public and verifiable, and include all key actors. If such a pause cannot be enacted quickly, governments should step in and institute a moratorium.
Despite the call for a pause, innovation in large language models continues on. Bloomberg’s 50-billion parameter large language model, purpose-built from scratch for finance was recently released. When will the first large language model for manufacturing be developed?
Just wait until you see the EngineeringGPT and PhysicsGPT merged with Image and Trained on building BPM2.0+ modeling. Going to be hitting a inventions per second calculation 🧮 in the next year.— Bitsbetrippin (@BitsBeTrippin) March 31, 2023
In the legacy of Gordon Moore, “Whatever has been done, can be outdone.”
“Whatever has been done, can be outdone.” —Gordon Moore (1929–2023)— Intel (@intel) March 25, 2023
This week's most influential Industry 4.0 media
In a World First, Yokogawa’s Autonomous Control AI Is Officially Adopted for Use at an ENEOS Materials Chemical Plant
ENEOS Materials Corporation (formerly the elastomers business unit of JSR Corporation) and Yokogawa Electric Corporation (TOKYO: 6841) announce they have reached an agreement that Factorial Kernel Dynamic Policy Programming (FKDPP), a reinforcement learning-based AI algorithm, will be officially adopted for use at an ENEOS Materials chemical plant. This agreement follows a successful field test in which this autonomous control AI demonstrated a high level of performance while controlling a distillation column at this plant for almost an entire year. This is the first example in the world of reinforcement learning AI being formally adopted for direct control of a plant.
Over a 35 day (840 hour) consecutive period, from January 17 to February 21, 2022, this field test initially confirmed that the AI solution could control distillation operations that were beyond the capabilities of existing control methods (PID control/APC) and had necessitated manual control of valves based on the judgements of experienced plant personnel. Following a scheduled plant shut-down for maintenance and repairs, the field test resumed and has continued to the present date. It has been conclusively shown that this solution is capable of controlling the complex conditions that are needed to maintain product quality and ensure that liquids in the distillation column remain at an appropriate level, while making maximum possible use of waste heat as a heat source. In so doing it has stabilized quality, achieved high yield, and saved energy.
Can Large Language Models Enhance Efficiency In Industrial Robotics?
One of the factors that slow down the penetration of industrial robots into manufacturing is the complexity of human-to-machine interfaces. This is where large language models, such as ChatGPT developed by OpenAI, come in. Large language models are a cutting-edge artificial intelligence technology that can understand and respond to human language at times almost indistinguishable from human conversation. Its versatility has been proven in applications ranging from chatbots to language translation and even creative writing.
It turns out that large language models are quite effective at generating teach pendant programs for a variety of industrial robots, such as KUKA, FANUC, Yaskawa, ABB and others.
Enabling Certification of DL-Based Software Components
Artificial-intelligence software, particularly deep-learning (DL) components, is currently the most advanced and economically feasible solution for achieving autonomous systems, such as autonomous cars. However, the nature of DL algorithms and their current implementation are at odds with the stringent software development process followed in safety-critical systems like cars, satellites and trains.
SAFEXPLAIN, a project funded by the European Union, aims to bridge this gap to enable the certification of DL-based software components, including those that inherit high-integrity fail-operational safety requirements. SAFEXPLAIN considers three pillars simultaneously:
- DL-based software components
- Certification practice against functional safety standards
- Efficient execution on commercial platforms
HAYAT HOLDING uses Amazon SageMaker to increase product quality and optimize manufacturing output, saving $300,000 annually
In this post, we share how HAYAT HOLDING—a global player with 41 companies operating in different industries, including HAYAT, the world’s fourth-largest branded diaper manufacturer, and KEAS, the world’s fifth-largest wood-based panel manufacturer—collaborated with AWS to build a solution that uses Amazon SageMaker Model Training, Amazon SageMaker Automatic Model Tuning, and Amazon SageMaker Model Deployment to continuously improve operational performance, increase product quality, and optimize manufacturing output of medium-density fiberboard (MDF) wood panels.
Quality prediction using ML is powerful but requires effort and skill to design, integrate with the manufacturing process, and maintain. With the support of AWS Prototyping specialists, and AWS Partner Deloitte, HAYAT HOLDING built an end-to-end pipeline. Product quality prediction and adhesive consumption recommendation results can be observed by field experts through dashboards in near-real time, resulting in a faster feedback loop. Laboratory results indicate a significant impact equating to savings of $300,000 annually, reducing their carbon footprint in production by preventing unnecessary chemical waste.
Better spinach through AI: Tokyo startup automates seedling selection
A Japanese agricultural startup has developed technology that uses artificial intelligence to assess the growth and potential of spinach seedlings, aiming to reduce food loss by increasing yields and efficiency.
The AI system has two parts. The first uses photographs to estimate the height, width and weight of seedlings grown in plant factories. The other predicts future growth using an index developed by Farmship. The first eliminates seedlings that are obviously not growing well, and the other narrows the remaining seedlings to only superior ones, making harvesting easier. In trials, the ratio of seedlings that grew properly increased to 80%, from 54% using standard methods. This corresponds to a 17% harvest increase.
Plant tour: Middle River Aerostructure Systems, Baltimore, Md., U.S.
Current production programs at MRAS include the LEAP-1A engine for the Airbus A320neo, LEAP-1C for the Comac C919, the CF-6 engine for multiple civil and military widebody aircraft, the Passport 20 engine for Bombardier’s Global 7500 business jet, the CF34-10A engine for the Comac ARJ21 and the GE9X engine for the Boeing 777X.
“For us, it was the integration with engineering, ERP and MRP that was key,” says Diederich. “Plataine integrates into all of this. It manages the raw materials coming in, generates cut plans per our engineering and marks the labels on the kit plies. We can dynamically nest up to 10 parts. The Plataine software uses AI to recommend which rolls of raw material should be cut next.” What is dynamic nesting? “Optimizing the nests on the fly as the software receives new inputs or when we query it,” says Diederich. “It can also send us alarms to change materials or operations. The sorted ply information is output to the Eastman systems, which have “cut and collect” software that identifies plies for kits using different colored lights. These match stacking tables at the conveyor’s end. ”
Bell’s Brewery: The Advanced Manufacturing of Beer With a Craft Brewing Spirit
The hiss of steam, hum of churning machines, and a fragrant aroma of hops provides the greeting to Bell’s Brewery production facility in Comstock, MI. The complex, which spans 200,000 square feet, brews more than 20 beers for distribution as the national production backbone of the Bell’s brand.
“Once we got the robot arm in place we were able to add a second shift as a result of putting this automation in. Compared to what people’s mindsets are that you take jobs away, we actually sped up our production and were able to add more jobs to our workforce.” On the digital side of automation, Sippel said he could run most of the brewery on his phone while lying on the couch at home. “In a lot of ways Automation can make it feel like you are playing [a video game like] Sim Brewer,” Sippel said. “It is very easy to forget you are controlling very real processes that can be really dangerous.” Operating with safety in mind in the digital realm is key, but it is also important to be present in the room, he added. “You still need to have physical eyes and ears in the process.”
Bell’s Comstock facility is more than meets the eye. The brewery features an 85-ton geothermal field installed eight feet beneath its two-acre hop yard, using glycol filled tubes to exchange heat and offset HVAC demand in the brewery’s offices. Additionally, the LED lights installed at the brewery conserve 180,000 kWh of electricity per year in comparison to incandescent bulbs— that’s enough electricity to power 22 homes in one year.
High Mix Low Volume Manufacturing Automation with Collaborative Applications
Unlocking the Value Potential of Additive Manufacturing
Transitioning to AM requires not only a change in mindset but more importantly, the ability to quickly and easily identify which parts are best suited for the additive manufacturing process. This is where AI and machine learning are now bridging the gap between traditional AM –where most of its value materializes in the form of functional prototypes – and more advanced additive manufacturing operations. “We have upwards of a million part numbers,” said Werner Stapela, head of global additive design and manufacturing at Danfoss – an international leader in drives, HVAC and power management systems. “So, it would be impossible for us to manually analyze each one to determine whether additive manufacturing would either add value or reduce costs.”
“We have been utilizing 3D printing for decades, mostly for prototyping, but the Castor3D software allows us to focus on our end components and more specifically the costs associated with that,” added Stapela. The software’s algorithm and machine learning can scan thousands of parts at once by analyzing CAD files. It evaluates five factors: materials, CAD geometry, costs, lead time and strength testing to identify suitable parts for AM. The software can also make design for additive manufacturing (DfAM) suggestions regarding part consolidation and weight reduction opportunities.
3D Printing A Bridge With A Twin
The world’s first 3D-printed steel bridge showcases technology that could reduce the amount of material used in structures. It has a network of sensors that continuously feed data into a ‘digital twin’; that will monitor how the bridge behaves over time and help refine the design of similar structures in future. Hugh Ferguson reports and looks at how a similar approach to monitoring is being adopted across civil engineering projects.
The origins of this bridge lie within a small creative design studio in Amsterdam, Joris Laarman Lab, headed by designer and artist Joris Laarman. In about 2014, excited by opportunities presented by emerging technologies, the team decided to develop designs in 3D-printed stainless steel. This presented an immediate challenge: no-one had before produced large steel objects using 3D printing or additive manufacturing. The process requires molten metal to be deposited in multiple layers. At the time, there were already tools for metal inert gas (MIG) welding. In this arc welding process, a continuous solid wire – usually 1.2 millimetre in diameter – is electrically heated and fed from a welding gun. There were also robots on which the tools could be mounted. However, no-one had used robots with MIG welding. Robots were generally used for repetitive ‘pick and place’ tasks, rather than complex welding control.
AutoDMP Finds Efficient Ways To Place Transistors On Silicon Chips
Macro placement is a critical very large-scale integration (VLSI) physical design problem that significantly impacts the design powerperformance-area (PPA) metrics. This paper proposes AutoDMP, a methodology that leverages DREAMPlace, a GPU-accelerated placer, to place macros and standard cells concurrently in conjunction with automated parameter tuning using a multi-objective hyperparameter optimization technique. As a result, we can generate high-quality predictable solutions, improving the macro placement quality of academic benchmarks compared to baseline results generated from academic and commercial tools. AutoDMP is also computationally efficient, optimizing a design with 2.7 million cells and 320 macros in 3 hours on a single NVIDIA DGX Station A100. This work demonstrates the promise and potential of combining GPU-accelerated algorithms and ML techniques for VLSI design automation
Dynamic state and parameter estimation in multi-machine power systems—Experimental demonstration using real-world PMU-measurements
Dynamic state and parameter estimation (DSE) plays a key role for reliably monitoring and operating future, power-electronics-dominated power systems. While DSE is a very active research field, experimental applications of proposed algorithms to real-world systems remain scarce. This motivates the present paper, in which we demonstrate the effectiveness of a DSE algorithm previously presented by parts of the authors with real-world data collected by a Phasor Measurement Unit (PMU) at a substation close to a power plant within the extra-high voltage grid of Germany. To this end, at first we derive a suitable mapping of the real-world PMU-measurements recorded at a substation close to the power plant to the terminal bus of the power plants’ synchronous generator. This mapping considers the high-voltage transmission line, the tap-changing transformer and the auxiliary system of the power plant. Next, we introduce several practically motivated extensions to the estimation algorithm, which significantly improve its practical performance with real-world measurements. Finally, we successfully validate the algorithm experimentally in an auto- as well as a cross-validation.
Weekly mergers, partnerships, and funding events across industrial value chains
Venti Technologies Announces $28.8 Million Series A Funding to Automate Global Logistics and Industrial Hubs
Venti Technologies, a world leader in autonomous logistics for global supply chain and industrial hubs, today announced that it has secured $28.8 million in a Series A financing. The financing round was led by LG Technology Ventures, the Silicon Valley-based venture capital arm of the LG Group, with participation from Safar Partners, UOB Venture Management, and existing investors Alpha JWC and LDV Partners. The funding will be used to accelerate Venti’s growth and meet increasing demand from customers worldwide.
TDK Ventures invests in pH7 Technologies for clean extraction and recycling of critical metals
TDK Corporation (TSE: 6762) announced today that subsidiary TDK Ventures Inc., its corporate venture-capital arm, is investing in sustainable metal extraction innovator pH7 Technologies and co-leading their Series A financing round. The unique chemical process developed by pH7 has the potential to change the metal extraction from mining and recycling resources through their sustainable and environmentally friendly approach. This closed-loop metal extraction process could potentially empower the metal transition necessary to usher in next generation global electrification. This investment reflects TDK Ventures’ commitment to supporting innovative solutions that can help address the global energy transformation by transforming the metal supply chain.
Cowboy Clean Fuels Completes Series A Financing to Advance Renewable Natural Gas Commercialization Plan
Cowboy Clean Fuels, LLC (“we,” “us,” “our,” or “the Company”), a premier energy technology company formed to produce carbon-negative, renewable natural gas (“RNG”) from readily available waste agricultural byproducts utilizing proprietary, patented technology, today announced the closing of a $7.5 million Series A financing led by Machan Investments, LLC and advised by Syren Capital, LLC. The capital will be used to clear regulatory pathway objectives, provide initial engineering design and prepare for the Company’s initial commercial-scale project launch in the Powder River Basin (“PRB”) of Wyoming.
Climate Tech Start-Up 3E Nano Closes US$4 Million Series Seed Funding Round, Secures C$5M in Funding from Sustainable Development Technology Canada
3E Nano, a technology start-up poised to disrupt the window market, is pleased to announce it secured a US$4 million Series Seed funding round. The syndicated raise was led by Energy Foundry and includes major investors MUUS Climate Partners, ACT Venture Partners, Creative Ventures, and New Climate Ventures. Additional climate impact investors participated in the round with significant contributions from Vectors Capital and VertueLab. 3E Nano’s patented thermal energy control nano-coating is a transparent, flexible, and low-cost solution that will transform the window market, quadrupling a window’s R-value, or insulating effectiveness.
Gen Phoenix partners with Material Impact, Dr. Martens, InMotion Ventures, and Tapestry to scale sustainable next-gen material innovation
Gen Phoenix, the leading producer of sustainable recycled leather at scale, is announcing $18M in funding to further the company’s mission to create premium and eco-conscious next-gen materials. The investment was led by venture capital firm Material Impact, with participation from Dr. Martens, InMotion Ventures, the investment arm of Jaguar Land Rover, and Tapestry, the house of modern luxury lifestyle brands that include Coach, Stuart Weitzman and Kate Spade. The funding round also includes existing investors ETF Partners and the Hermes GPE Environmental Innovation Fund, who continue to support the company’s growth plans.
Finnish startup Carbonaide lands €1.8 million to make carbon-negative concrete manufacturing possible
VTT spin-out Carbonaide is on a mission to make manufacturing concrete carbon negative, revolutionizing a billion-dollar industry. The Finnish startup has just secured €1.8 million to scale.