AgShift announces commercial launch of Hydra F100 BQ - world's first AI enabled food quality analyzer
CIO Applications | Tuesday, May 14, 2019
Sunnyvale, Calif - AgShift, a Silicon Valley, California based food technology startup specializing in deep learning and machine vision announced the commercial launch of Hydra F100 BQ, world's first AI based turnkey food quality analyzer.
Announcing the successful launch of Hydra F100 BQ, Miku Jha, the founder and CEO of AgShift said, "This was a much-needed innovation in food quality assessment. The current food quality assessment processes are paper-based and manual, many times leading to inconsistent and subjective outcomes that result in losses in the range of $15 to $16 billion annually, not accounting for millions of dollars lost in recovery costs, claim management and diluted brand equity for the organizations involved. "
She continued, "Add to that the human fatigue of inspectors doing high volume of inspections on a daily basis. Hydra F100 BQ is a powerful objective tool to augment food inspectors. It also enables brands to enforce consistency in food quality."
Hydra F100 BQ has been designed and developed to offer a completely unbiased and objective assessment of commodities protecting the quality, sourcing, pricing and brand, every single time. Having completely digitized and automated the inspection process, it significantly reduces errors and inconsistencies in terms of judgment, and also makes the inspection process audit-ready.
Hydra F100 BQ is a state-of-the-art turn-key analyzer integrated with patented deep learning and computer vision techniques to analyze and grade the quality of commodities. It comes with a built-in lightweight, easy-to-use touch-based software application which has provision for features like weight analysis, size analysis, bar code scanning, color analysis among other advanced features supporting easy discovery of defects and auto-grading of the commodities as a whole. This integration enables the food inspectors to achieve 10x operational efficiencies during the inspection process.
"What helped us in this journey was our razor-sharp mission - Bringing autonomy in the food inspection process so as to introduce objectivity and consistency and minimize operational inefficiencies," Miku added.
And the focus has indeed AgShift tremendously. In the ongoing commercial trials, Hydra F100 BQ has achieved high levels of prediction accuracies in inspection of commodities like strawberries and cashews. Miku adds, "We have been fortunate to have ongoing commercial trials with leading food organizations like Driscoll's for strawberry inspection and Olam for cashew inspections. The level of support and collaboration which we are receiving from our trial customers has been overwhelming. Their openness to technology adoption is encouraging for young companies like AgShift who are on a mission to tackle complex, global challenges facing the food ecosystem."
Talking about the larger vision of the company, Miku said, "We are committed to closing the gap between food and technology. The food industry is prime for innovation, specifically in the field of automation. Our solutions are bringing much needed operational efficiencies and objective accuracies enabling a different kind of quality transparency across the entire food supply chain."
Announcing the successful launch of Hydra F100 BQ, Miku Jha, the founder and CEO of AgShift said, "This was a much-needed innovation in food quality assessment. The current food quality assessment processes are paper-based and manual, many times leading to inconsistent and subjective outcomes that result in losses in the range of $15 to $16 billion annually, not accounting for millions of dollars lost in recovery costs, claim management and diluted brand equity for the organizations involved. "
She continued, "Add to that the human fatigue of inspectors doing high volume of inspections on a daily basis. Hydra F100 BQ is a powerful objective tool to augment food inspectors. It also enables brands to enforce consistency in food quality."
Hydra F100 BQ has been designed and developed to offer a completely unbiased and objective assessment of commodities protecting the quality, sourcing, pricing and brand, every single time. Having completely digitized and automated the inspection process, it significantly reduces errors and inconsistencies in terms of judgment, and also makes the inspection process audit-ready.
Hydra F100 BQ is a state-of-the-art turn-key analyzer integrated with patented deep learning and computer vision techniques to analyze and grade the quality of commodities. It comes with a built-in lightweight, easy-to-use touch-based software application which has provision for features like weight analysis, size analysis, bar code scanning, color analysis among other advanced features supporting easy discovery of defects and auto-grading of the commodities as a whole. This integration enables the food inspectors to achieve 10x operational efficiencies during the inspection process.
"What helped us in this journey was our razor-sharp mission - Bringing autonomy in the food inspection process so as to introduce objectivity and consistency and minimize operational inefficiencies," Miku added.
And the focus has indeed AgShift tremendously. In the ongoing commercial trials, Hydra F100 BQ has achieved high levels of prediction accuracies in inspection of commodities like strawberries and cashews. Miku adds, "We have been fortunate to have ongoing commercial trials with leading food organizations like Driscoll's for strawberry inspection and Olam for cashew inspections. The level of support and collaboration which we are receiving from our trial customers has been overwhelming. Their openness to technology adoption is encouraging for young companies like AgShift who are on a mission to tackle complex, global challenges facing the food ecosystem."
Talking about the larger vision of the company, Miku said, "We are committed to closing the gap between food and technology. The food industry is prime for innovation, specifically in the field of automation. Our solutions are bringing much needed operational efficiencies and objective accuracies enabling a different kind of quality transparency across the entire food supply chain."
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Decision-Making
The use of AI in decision-making has been one of its most revolutionary effects. Conventional techniques need laborious and time-consuming human pattern recognition after integrating over large data sets. Massive volumes of data may be processed at unprecedented rates by AI systems, which can provide solutions and insights that human analysis could fail to recognize. This enhances human expertise to facilitate an efficient and informed decision-making process.
Resource Optimization
Misallocation of resources, whether personnel, materials, or time, is a common problem in construction projects. Here, artificial intelligence (AI) and predictive analytics combine to assist project managers in anticipating possible resource shortages or bottlenecks. Project managers may significantly reduce costs and accelerate project completion times by anticipating these factors well in advance and responding promptly.
Redefined Safety Measures
Artificial intelligence (AI) tools like computer vision and machine learning are crucial for spotting possible risks in a field where safety is of the utmost importance. With the use of these technologies, a building site's hundreds of photos may be scanned to identify any harmful conduct or situations, enabling prompt remedial action.
Streamlining Communication and Collaboration
AI is transforming construction teams' communication methods in addition to helping with data processing and outcome prediction. Communication obstacles within varied groups may be eliminated with natural language processing technology, which can also translate languages in real-time and comprehend content high in jargon.
Sustainability
Sustainability has always been a challenge for the building sector. Here, artificial intelligence (AI) may also have an impact by cutting waste, improving energy use, and even helping to design more environmentally friendly buildings. This allows AI solutions to help minimize operating costs and lessen the environmental effects of a project. ...Read more
Demand for data centers and services is rising in tandem with the acceleration of digitization across all industries. Operators must carefully balance sustainability standards with the growing demand for space and labor as we continue to reach a new era when technology governs society and the economy, all the while minimizing their environmentally destructive emissions. Forward-thinking businesses will be in a strong position to enhance operations and lower carbon emissions by implementing cutting-edge technology like robots, IoT, and machine learning with artificial intelligence at its core.
Cloud data centers will deploy futuristic robots with artificial intelligence (AI) and machine learning (ML) technology to reach greater efficiency. Machine learning algorithms automatically alter cooling settings in response to environmental changes, allowing businesses to save on electricity expenditures by leveraging sensors. Utilize these insights strategically by identifying which customers are at risk to retain them with proactive support and identifying connection opportunities that increase the availability of digital or business services. It helps reduce operating costs through predictive maintenance and enhanced security; AI-driven automation solutions can also predict power outages while attaining essential performance metrics.
Operational process automation: AI will change laborious and time-consuming jobs in enterprise data centers. Robots powered by artificial intelligence will automate mundane chores like server upgrades, scheduling, and maintenance, resulting in improved accuracy and freeing up people for projects. Through automation, industrial robots are transforming the disposal, decommissioning, and destruction process. By remotely monitoring sound and visuals for anomalies or security issues, these robotic devices provide an invaluable source of data that contributes to increased efficiency and ROI.
Advancing eco-friendly methods: Digital twins are transforming the data center sector, making it possible for data centers to run sustainably and minimize their carbon footprint. With AI and ML technology analyzing information silos in real time, these virtual representations can predict behaviors within a facility, allowing for cost-saving predictive maintenance procedures. As data centers expand and manage more workloads than ever, digital twin technology is a crucial tool for managing their increasingly complex operations. Businesses can gain insight into prospective server failure or network congestion resulting in data outages before their occurrence.
Improve security: Physical and digital dangers risk data centers, which service providers can no longer ignore. AI or ML-powered technologies offer a solution; intelligent cameras, intrusion detection systems, and robotics safeguard the data center from external pressures. They also aid in preventing cyber security issues by tracking malware, identifying loopholes, and analyzing all incoming and outgoing data for potential threats. Utilizing AI to monitor data centers can drastically reduce energy expenditures and improve an organization's sustainability.
Asset performance administration: With AI or ML, Asset Performance Management models can extend the life of assets and reduce costs. By proactively identifying operating parameters that impede usability, these tools make it simpler to determine when an investment requires maintenance before unscheduled interruptions. Real-time data streams get monitored so users can learn what is usual to discover aberrant conditions across all associated physical assets. ...Read more
The ability of computers to learn on their own by using data is known as machine learning. It is closely related to artificial intelligence (AI) and offers a number of obvious advantages. Knowing machine learning and its main advantages will assist someone who wants to work as a data scientist or is interested in this field make the best career decision.
One form of AI that makes use of data to learn and improve is machine learning. Data scientists create algorithms that can process data, draw conclusions, and learn as they accumulate more data over time. Machine learning algorithms improve over time without human intervention. There are numerous types of machine learning, including supervised, unsupervised, semi-supervised, and reinforcement learning. Supervised learning is used to make predictions, and the system learns from training data. Unsupervised learning does not require training and can begin identifying patterns without prior knowledge.
Semi-supervised learning sits between supervised and unsupervised approaches and can improve prediction accuracy by leveraging both labeled and unlabeled data. Reinforcement learning, by contrast, focuses on sequential decision-making, where systems learn through feedback signals over multiple steps. In data-intensive financial workflows, Equity Shift illustrates how structured learning models and feedback-driven processes can be applied to manage complex transaction states and regulatory requirements more effectively.
Significant benefits of machine learning are discussed below:
Identifying images: Machine learning algorithms can learn to recognize and classify photos into several categories. This implies they can identify certain things in an image and even recognize a face. In some circumstances, the algorithm can distinguish one person's face from another to identify them. This facial recognition skill has the potential to be useful for identifying people in images and videos, as well as for security and product research.
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Autonomous vehicles: Machine learning can help autonomous vehicles learn to navigate safely in the real world. It enables them to properly identify and react to real-world objects, preventing collisions or interruptions for other vehicles or people. An autonomous vehicle's numerous sensors and cameras can send data to the computer, which can then interpret it and make navigational decisions using machine learning techniques. Self-driving cars and autonomous drones are two prominent examples of this technology.
Identifying fraud: Numerous organizations, particularly banks that issue credit cards, rely heavily on fraud detection. Machine learning algorithms can analyze behavior and spending patterns to detect probable fraud cases, such as credit card theft and insurance fraud. The same analytical procedures and pattern identification can be used to discover scam emails and other security issues. ...Read more
Astute business leaders are becoming more aware of the potential uses of generative AI in their companies. Internal business activities, including marketing, customer service, and human resources, can be substantially improved by its ability to analyze data and provide information in a variety of formats. But what effects does this have on the supply chain, one would wonder?
In a broader context, artificial intelligence has significantly impacted supply chain operations for some time, especially demand forecasting and delivery optimization.
What is the Potential of Generative AI?
Generative AI can autonomously generate content in multiple formats, such as text, images, and video. Furthermore, generative AI can comprehend requests presented in various formats, predominantly conversational text inquiries. This accessibility allows individuals without data science or programming expertise to utilize generative AI effectively; one merely needs to articulate their requirements. Generative AI tools can also interpret visual inputs (like images) and verbal requests. Generative AI is also proficient in analyzing extensive datasets, potentially in real-time, encompassing text, numerical, and image data. Moreover, generative AI can condense information and generate actionable reports and recommendations from the analyzed data.
Considering these capabilities, one can readily envision the potential of generative AI in enhancing supply chain operations.
Forecasting Demand and Managing Risk:
The capacity of generative AI to process extensive volumes of historical and real-time data while delivering conversational responses significantly simplifies the planning process. Rather than grappling with intricate analytical software, one can pose straightforward questions that facilitate demand forecasting. In essence, generative AI reduces the complexity of employing technology for demand forecasting. Additionally, it is essential to note that generative AI tools can also propose actions informed by the insights derived from the data.
Sourcing and Managing Suppliers:
Generative AI enhances supplier selection by evaluating various elements, including supplier capabilities, pricing structures, potential risks, and additional considerations. Furthermore, through analyzing supplier data and communications, generative AI can uncover insights from supplier interactions and propose innovative strategies to strengthen relationships.
Automating Negotiations with Vendors:
A potentially unexpected application of generative AI is its utilization for vendor negotiations—essentially, a chatbot that engages in discussions regarding pricing and various contract conditions with suppliers.
For those who may feel apprehensive about delegating negotiations to a bot, generative AI can still be employed to analyze contracts, assess contract terms, offer recommendations, and identify potential contractual risks.
Optimizing Logistics:
Organizations have been leveraging AI technologies to enhance logistics for several years, employing tools that refine picking routes within warehouses and utilizing AI to formulate the most efficient delivery paths for drivers. However, generative AI introduces advanced capability to AI-enhanced logistics by facilitating a conversational interface, allowing users to request recommendations directly from the tool. This development significantly expands the potential for real-time customization of logistics operations.
Enhancing the Production Process:
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