Vikhyat Chaudhry is the CTO, COO and co-founder of Buzz Solutions and a former data scientist at Cisco, a machine learning/embedded systems engineer at Altitude and a Stanford graduate.
Buzz Solutions delivers accurate AI and predictive analytics software to power more efficient visual inspections for transmission, distribution, and substation infrastructure.
Are you able to share your journey and profession highlights that led you to Co-Found Buzz Solutions?
I grew up in Recent Delhi, India, with a natural curiosity for innovation and engineering and I attended the Delhi College of Engineering where I studied Civil and Environmental Engineering. I particularly remember a moment during my final yr after I built a drone from scratch and flew it in the town. The task was to observe air pollution in Recent Delhi and thru this experiment, I discovered that the standard was above 500 AQI, which is the equivalent of smoking 60 cigarettes a day. The poor air quality may very well be directly traced to an absence of electrification, rising vehicular emissions and increased variety of coal-powered power plants over time. This experience solidified my interest in using technology to handle real-world problems related to energy and power.
Before founding Buzz, my technology background led me to my role because the Lead of Machine AI and Data Science Teams at Cisco Systems for a number of years. This experience was invaluable and built my exposure to a various range of artificial intelligence and machine learning projects early on.
I received my masters in Civil/Environmental Engineering from Stanford University in 2016. During this time I took classes specializing in energy engineering, constructing my interest that began overseas. I met my co-founder Kaitlyn in a category where we bonded over our passions for the environment, energy and entrepreneurship. We stumbled upon an important need within the utility industry and have been working on solutions to handle it ever since.
What key developments have you ever observed within the progression from traditional AI to Generative AI during your profession, and what significant impacts has this transition had on various industries?
In 2022, we began experimenting with Generative AI. GenAI within the utility sector is an interesting use case because the information we work with involves many alternative variables. There are aspects like camera resolution, angle of capture, and object distance – and people are only for the drones. There are also environmental conditions like corrosion or vegetation encroachment that introduce quite a few degrees of freedom. For this reason complexity, good training data for grid models might be hard to come back by.
That’s where GenAI has are available in over the past few years – as artificial intelligence and machine learning improve, so do the training sets it creates.
GenAI has grow to be a viable option for training models, especially with crucial ‘edge cases’ where variables have more extreme values, comparable to within the case of a wildfire. As GenAI within the utility industry progresses, synthetic data sets, based on real world data, will assist in further training models to handle complex and unique data scenarios more effectively, offering significant improvements in predictive maintenance and anomaly detection which can in turn reduce natural disasters.
Are you able to elaborate on how Buzz Solutions’ AI tool uses real data for anomaly detection and the advantages it offers over synthetic data?
Within the utility industry, real data means whatever might be captured in the sphere, often including images or video taken from aerial sources like drones or helicopters. Synthetic data, alternatively, is data collected through a picture replication process that manually alters various components of a picture to try to account for an exponential amount of scenarios and edge cases. Currently, it’s great on paper but not in practice. Models trained with real data from the beginning are proven to be more accurate and the advantage is that through the usage of real data, teams can map 1:1 with the ‘ground truth’ – an accurate representation of the physical world scenarios a technician is more likely to encounter (like background noise and weather). The actual data accounts for real-world possibilities, and includes the unpredictable variables of fault detection.
While synthetic data alone will not be capable of optimize for real-world scenarios (yet), it still plays a very important role in training models.
What are the most important challenges you face when integrating AI with legacy systems in utility corporations?
Legacy systems in utility corporations are sometimes incompatible with AI advancements. Two major challenges we see corporations face are internal transformation and data management. Siloed data and communication might be detrimental to digital transformation efforts. The information that utilities already possess should be managed and secure while information is carried over.
Moreover, utilities that also use on-premises data storage face larger challenges. The shift from on-premises data storage to cloud infrastructure will not be the problem, but relatively the extensive transformation and aftershock that follows. This process demands substantial resources and time, making it difficult so as to add different technologies on top of the transition. Introducing effective AI solutions will not be really helpful until this process is complete.
It’s also essential that internally, there’s a cultural shift together with the technology shift. This requires having employees on board with continuous learning and flexibility to changes in the method and searching at AI solutions as effective tools to make their day-to-day jobs easier and efficient.
Are you able to explain the means of training AI models with field-tested data from vital infrastructure sites?
An enormous a part of the training process is ingesting the aerial data provided by drones and helicopters. We elect to make use of drones over methods like satellites as a consequence of the pliability and immediate data delivery that they permit. We use three primary various kinds of algorithms: image clustering, segmentation, and anomaly detection.
Our technology is driven by Human-in-the-loop machine learning – which allows subject material experts on our team to present direct feedback to the model for predictions below a certain level of confidence. We’re lucky to have the SMEs on our teams that we do – with their many years of combined field technician experience, they supply feedback to make our models more accurate, personalized, and robust.
Through the use of real field-tested data, we will make sure that our anomaly detection is very accurate and reliable, providing utility corporations with actionable insights.
How does Buzz Solutions’ AI technology contribute to creating power line repairs safer?
Power line repair work is considered one of the deadliest occupations in America, and the industry is experiencing the results of an aging workforce and technician shortages.
With our technology, PowerAI, emergency response has been made more practical and accurate, in order that technicians can assess damage remotely and have time to develop a predetermined plan of action – which reduces the potential of sending in a technician to an unknown, potentially dangerous situation.
PowerAI uses computer vision and machine learning to automate an enormous portion of the fault detection process. It has made the evaluation of huge masses of information points faster, safer, and cheaper, so now the technicians face reduced unnecessary risk and better operational efficiency. This operational efficiency presents itself through smaller costs, quicker turnaround times, and preventative maintenance.
What role do drones and other advanced technologies play in modernizing infrastructure inspections?
Historically, the means of infrastructure inspections was completely manual and really mundane. Inspectors would sit in front of the pc screen, shuffle through 1000’s of images, and discover issues by hand. This process became unsustainable when power lines kept experiencing issues resulting in more unsafe situations and better regulatory overviews, increasing the quantity of information needed to be reviewed in a shorter period of time.
AI-based technology significantly streamlines the means of analyzing data, which reduces the time and price involved. This permits utility corporations to deploy repair teams more quickly and effectively. The detection of issues can be lots more precise, ensuring that repairs are timely and stopping burgeoning hazards.
In capturing images for evaluation, drone inspections are safer and more cost effective than other methods of infrastructure like helicopters, satellites, and fixed-wing aircrafts. Their portability allows them to maneuver in a way that they will get close and capture more granular information.
How does Buzz Solutions’ AI-powered platform help utility corporations with predictive maintenance and price savings?
Our solution takes a lot of the manual evaluation work out of grid inspection. PowerAI can quickly discover dangerous situations to forestall potential disasters and supply critical information for monitoring and security purposes. The AI algorithms are trained to discover anomalies like extreme temperatures, unauthorized vehicle access/personnel, thermal imaging, and more.
On top of preventive tracking, PowerAI also can provide tiered prioritization of anomalies for optimized maintenance planning. All of this stuff minimize the necessity for physical inspections, reducing operational costs and safety risks related to manual inspections. The AI-powered platform also provides more precise and accurate detection, improving maintenance decisions.
Are you able to discuss the impact of adopting AI on the operational efficiency of utility corporations?
After the initial lift of adopting an AI model, a utility company will proceed to reap the advantages of the model for an limitless period of time. The lifecycle of an AI model begins at installation. AI can harvest actionable insights from 1000’s of images taken across a whole lot of miles of infrastructure. Considering that we received our first dataset from a utility on a tape, that is extraordinary and it’s only getting smarter. AI makes early detection of maintenance issues far more possible, which prevents minor incidents from escalating into larger safety hazards like wildfires and serious injuries. It reduces the necessity for human inspections, making the utility more cost effective.
In your article “Adopting AI Is Just The Starting For Utility Firms,” you discuss the initial steps of AI adoption. What are essentially the most critical considerations for utilities starting their AI journey?
There is a large opportunity for utilities to make use of AI, and plenty of solutions on the market to think about. Before jumping in, it’s essential to discover your goals and set a stable foundation – what challenges are you currently facing that you want to AI to assist address? Does your team possess the technical expertise and time to tackle such a posh overhaul? How will it impact your customers?
On top of being aligned internally is being prepared to get more data than the utility has previously, which can likely result in more maintenance as issues arise. A utility must have a plan to accommodate these requests and make sure that they’ve the right resources before starting their AI journey. Utilities also have to work with solution providers to implement the proper data access, privacy and security when deploying AI solutions. AI-generated insights should finally be fed into existing utility workflows in order that they grow to be actionable and may meet the business and operational goals of the organization.
