Mediaboss Marketing

Marijuana use and possession of up to 6 ounces of weed are legal in New Jersey if you are at least 21 years of age.Driving while high isnt.

Pot can be found in your system by a blood test. So if you get into an accident, a court could order you to be tested, especially when that accident causes injuries or fatalities. Thats something Danielle Bowker, 30, of Toms River found out on June 21 when Ocean County Prosecutor Bradley D. Billhimer announced she had been charged with two counts of vehicular homicide, two counts of strict liability vehicular homicide and two counts of assault by auto as well as driving while intoxicated.

All of the charges were in connection with a motor vehicle crash that occurred in Manchester Township on March 29. At approximately 7:15 that morning, Manchester Township police were summoned to the area of Whitesville Road and Route 571 for a report of a motor vehicle crash with a fatality. It was a four-vehicle crash.

An investigation conducted by the Ocean County Prosecutors Office Vehicular Homicide Unit, Manchester Township Police Department and Ocean County Sheriffs Office Crime Scene Investigation Unit revealed that a 2018 Honda Civic operated by Bowker was traveling westbound on Route 571 when she failed to maintain her lane of travel while negotiating a right-hand curve. The Honda Civic then struck a state Department of Transportation Ford F-550 pickup truck operated by Eduardo Rivera, 30, of Hamilton Township, which was traveling eastbound on Route 571; Daniel Septor, 26, of the Cream Ridge section of Upper Freehold Township was a passenger in the Ford-550.

As a consequence, the Ford-550 lost directional control and struck a 2012 Toyota Camry operated by Michael Sadis, 48, of Toms River, pushing the Camry off the roadway into an embankment.

The Ford-550 continued in the same direction of travel and struck a 2015 Toyota Corolla operated by Paul Lamberti, 58, also of Toms River.

As a result of the crash, Sadis was pronounced deceased at the scene. Lamberti was airlifted to Jersey Shore University Medical Center in Neptune, where he ultimately succumbed to his injuries. Rivera and Septor were transported to Community Medical Center in Toms River for treatment of minor injuries.

Bowker also sustained minor injuries from the crash and was taken to Community Medical Center for treatment. While at Community Medical Center, a blood draw was taken from Bowker pursuant to a court-authorized warrant. Laboratory results of Bowkers blood draw, received by the Ocean County Prosecutors Office Vehicular Homicide Unit, revealed Bowker had an active THC (marijuana) level of 7 nanograms (ng) with a metabolite THC level of 61ng indicating Bowker was a recent, active user of marijuana at the time of the crash.

Upon reviewing the laboratory results of Bowkers blood draw, the states psychopharmacologist rendered an opinion that at the time of the crash, Bowkers faculties were impaired due to the effects of marijuana intoxication, and she could not safely operate a motor vehicle.

In light of the foregoing, arrangements were made for Bowker to surrender to Manchester Township Police Headquarters in the presence of her attorney on June 21. She was transported to the Ocean County Jail, where she is presently lodged pending a detention hearing.

Billhimer commended the Ocean County Prosecutors Office Vehicular Homicide Unit, Manchester Township Police Department, Ocean County Sheriffs Office Crime Scene Investigation Unit, Lakewood Township Police Department and Ocean County Medical Examiners Office for their combined and cooperative efforts in connection with this investigation.

As is usual, the prosecutors office press release ended with this statement:The charges referenced above are merely accusations and the press and public are reminded that all defendants are presumed innocent unless and until proven beyond guilty beyond a reasonable doubt in a court of law.

That statement could prove valuable in this case. Bowker and her attorney could mount a vigorous defense if the results of the blood draw are the only evidence the prosecution team has to offer. Marijuana can stay in ones system for a month. The law is still murky when it comes to what level of high constitutes driving under the influence of pot.

All 50 states have established a blood-alcohol level of 0.08% or higher as triggering a DUI charge, but there is no single standard when it comes to marijuana DUI charges.

There are at least six states that have legal THC limits, expressed in terms of nanograms per milliliter. Colorado, Montana and Washington have limits of 5ng/ml while Nevada and Ohios limits are 2ng/ml. Pennsylvanias limit is 1ng/ml. New Jersey apparently doesnt have a THC limit on its books.

If Bowker can afford to hire expert witnesses to contest the finding of the prosecutions expert witnesses, such as the states psychopharmacologist, a jury trial could prove very confusing and interesting.

The Ocean County Prosecutors Office didnt respond by press time to an inquiry about the Bowker press release. That inquiry didnt ask if there had been other evidence, such as an admission to using marijuana before driving, the presence of marijuana or drug paraphernalia in Bowkers vehicle, or the testimony of a drug recognition expert a law enforcement officer trained to administer tests to suspected impaired drivers to see in an individual is indeed impaired and if so, to categorize the type of impairment substance following a 12-step protocol. Law enforcement typically doesnt release such information before a trial.

The inquiry simply asked if the there had been a conviction for vehicular homicide in New Jersey based on the results of a blood test alone and attempted to confirm the state doesnt have a THC limit. A long internet search couldnt find either.

One thing is certain: Driving in New Jersey with marijuana in your bloodstream can get you in hot water even if you avoid conviction. An arrest, time in jail, attorney costs they all add up.

And you yourself could be killed.

Rick Mellerup

rickmellerup@thesandpaper.net

See more here:
Woman Charged With Vehicular Homicide Because of Marijuana in Her System - The SandPaper

HANCEVILLE, Ala. Wallace State Community College had 13 students earn a medal, including three teams to win a gold, at the 2022 SkillsUSA National Leadership and Skills Conference competition recently held in Atlanta.

Gold medal-winning teams for Wallace State were:

Wallace State students have won 12 national gold medals in the past three national SkillsUSA events combined.

JaQuane Brown and Nolan West earned a silver medal in Robotics: Urban Search and Rescue for Wallace State.

Winning a bronze medal were Robert Combs, Oliver Edge and Grant Wiley in Automated Manufacturing Technology and Ayla Dewald in Criminal Justice.

Im very proud of the students who competed at the SkillsUSA Nationals event this year. To win gold in three events, one in silver and two in bronze speaks to the quality of students and instruction we have at Wallace State, said Wes Rakestraw, Wallace States dean of Applied Technologies. SkillsUSA provides a great opportunity for our students to compete and showcase their talent and skill. We made the most of that opportunity this year.

Each Wallace State winner at the national level was a gold winner at the state SkillsUSA competition, which was held last month in Birmingham. The college set a record with 33 gold medals, among the 50 won at state.

Wallace States Criminal Justice Department was on the cusp of winning a national gold medal last year and in 2019. Aguliar, Fletcher and Maddox helped the Crime Scene Investigation team break through.Aguilar is from Boaz, Fletcher from Cullman and Maddox from Hartselle.

While the individuals change, our students have worked hard for seven years to earn a national gold medal. This group pulled it off, and Im so thrilled for them. Theyre awesome people to be around. They respect each other and pull for each other, and that makes it even more gratifying, said Dr. Thea Hall, Criminal Justice department chair. Theres no way to describe the joy on their faces when the names were announced.

Among the tasks to complete at nationals, the Crime Scene Investigation team had to observe a crime scene lab, presenting three interpretations, matching and dusting fingerprints, analyzing a skull entry and exit wound, conducting a DNA blood swab and more.

Daniel and Hollis gold-winning performance in Mobile Robotics Technology marked the third straight national competition that Wallace States Computer Science students walked away with a gold (Sara Eskew and Zach Hudson in 2021; Chase Blakey and Benjamin Brownlee in 2019). Daniel is from Holly Pond and Hollis from Cullman.

Mobile Robotics Technology teams had to put together an engineering notebook and explain the design, programming and functionality of a robot in addition to earning points for programming and driving skills.

It speaks volumes that our students have the desire to excel. The entire process is hard work. It takes months of practice and dedication to be successful at the state and national levels, and our students embrace it, said Terry Ayers, Wallace States Computer Science chairperson.

In Robotics and Automation Technology, Davis and Raia are recent graduates of Wallace States Mechantronics program. Both participated in the Alabama Federation for Advanced Manufacturing Education (FAME) program at Wallace State, allowing them work at Kamtek in Birmingham, while also completing their degrees. They both maintained employment at Kamtek. Davis is from Shelby and Raia from Columbiana.

It was a great day for all the gold medal winners across campus. I believe the on-the-job experience that Camden and Juddson have received benefited them through the SkillsUSA process.

Their work as industry apprentices enhanced their experience and knowledge, said Jerry Murcks, Wallace States Mechatronics instructor and chair.

For the silver-winning team in Robotics: Urban Search and Rescue, Brown is from Birmingham and West from Eva.

For the bronze-winning team in Automated Manufacturing Technology, Combs and Edge are both from Cullman and Wiley is from Warrior.

Dewald is from Arab.

See the entire list of winners | https://www.skillsusa-register.org/rpts/EventMedalists.aspx.

Read more:
Wallace State Community College has 13 students win National SkillsUSA medals, including three teams earning gold - The Cullman Tribune

The automotive industry is steadily moving away from internal combustion engines (ICEs) in the wake of more stringent regulations. Some industry watchers regard electric vehicles (EVs) as the next step in vehicle development, despite high costs and infrastructural limitations in developing markets outside Europe and Asia. However, many markets remain deeply dependent on the conventional ICE vehicle. A 2020 study by Boston Consulting Group found that nearly 28% of ICE vehicles could still be on the road as late as 2035, while EVs may only account for 48% of vehicles registered on the road by this time as well.

For manufacturers, this represents a huge and multi-faceted challenge. There are not only the industrys looming and ambitious environmental targets to consider but also the drive for CASE (Connected, Autonomous, Shared and Electric) vehicles is increasing design and development complexity. Also, there are the bottom-line pressures where European R&D spend has already increased by 75% between 2011 and 2019. Enter Secondmind, a machine learning company based in the UK. The company works with automotive engineers, helping them to use data-efficient transparent machine learning that combines the subject matter expertise of today's engineers with algorithmic intelligence. Secondmind's Chief Executive Gary Brotman argues that this new breed of machine learning is required to efficiently streamline the vehicle development process, helping automotive companies accelerate the transition away from ICE and ensure sustainable design and development engineering.

Read the original post:
Can machine learning clean up the last days of ICE? - Automotive World

Deep learning (DL) could be defined as a form of machine learning based on artificial neural networks which harness multiple processing layers in order to extract progressively better and more high-level insights from data. In essence it is simply a more sophisticated application of artificial intelligence (AI) platforms and machine learning (ML).

Here are some of the top trends in deep learning:

Model Scale Up

A lot of the excitement in deep learning right now is centered around scaling up large, relatively general models (now being called foundation models). They are exhibiting surprising capabilities such as generating novel text, images from text, and video from text. Anything that scales up AI models adds yet more capabilities to deep learning. This is showing up in algorithms that go beyond simplistic responses to multi-faceted answers and actions that dig deeper into data, preferences, and potential actions.

Scale Up Limitations

However, not everyone is convinced that the scaling up of neural networks is going to continue to bear fruit. Roadblocks may lie ahead.

There is some debate about how far we can get in terms of aspects of intelligence with scaling alone, said Peter Stone, PhD, Executive Director, Sony AI America.

Current models are limited in several ways, and some of the community is rushing to point those out. It will be interesting to see what capabilities can be achieved with neural networks alone, and what novel methods will be uncovered for combining neural networks with other AI paradigms.

AI and Model Training

AI isnt something you plug in and, presto, instant insights. It takes time for the deep learning platform to analyze data sets, spot patterns, and begin to derive conclusions that have broad applicability in the real world. The good news is that AI platforms are rapidly evolving to keep up with model training demands.

Instead of weeks to learn enough to begin to function, AI platforms are undergoing fundamental innovation, and are rapidly reaching the same maturity level as data analytics. As datasets become larger, deep learning models become more resource-intensive, requiring a lot of processing power to predict, validate, and recalibrate millions of times. Graphics Processing Units (GPUs) are advancing to handle this computing and AI platforms are evolving to keep up with model training demands.

Organizations can enhance their AI platforms by combining open-source projects and commercial technologies, said Bin Fan, VP Open Source and Founding Engineer atAlluxio.

It is essential to consider skills, speed of deployment, the variety of algorithms supported, and the flexibility of the system while making decisions.

Containerized Workloads

Deep learning workloads are increasingly containerized, further supporting autonomous operations, said Fan. Container technologies enable organizations to have isolation, portability, unlimited scalability, and dynamic behavior in MLOps. Thus, AI infrastructure management would become more automated, easier, and more business-friendly than before.

Containerization being the key, Kubernetes will aid cloud-native MLOps in integrating with more mature technologies, said Fan.

To keep up with this trend, organizations can find their AI workloads running on more flexible cloud environments in conjunction with Kubernetes.

Prescriptive Modeling over Predictive Modeling

Modeling has gone through many phases over the last many years. Initial attempts tried to predict trends from historical data. This had some value, but didnt take into account factors such as context, sudden traffic spikes, and shifts in market forces. In particular, real-time data played no real part in early efforts at predictive modeling.

As unstructured data became more important, organizations wanted to mine it to glean insight. Coupled with the rise in processing power, suddenly real time analysis rose to prominence. And the immense amounts of data generated by social media has only added to the need to address real time information.

How does this relate to AI, deep learning, and automation?

Many of the current and previous industry implementations of AI have relied on the AI to inform a human of some anticipated event, who then has the expert knowledge to know what action to take, said Frans Cronje, CEO and Co-founder of DataProphet.

Increasingly, providers are moving to AI that can anticipate a future event and take the correspondent action.

This opens the door to far more effective deep learning networks. With real time data being constantly used by multi-layered neural networks, AI can be utilized to take more and more of the workload away from humans. Instead of referring the decision to a human expert, deep learning can be used to prescribe predicted decisions based on historical, real-time, and analytical data.

See original here:
5 Top Deep Learning Trends in 2022 - Datamation

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Register today!

Artificial intelligence (AI) adoption keeps growing. According to a McKinsey survey, 56% of companies are now using AI in at least one function, up from 50% in 2020. A PwC survey found that the pandemic accelerated AI uptake and that 86% of companies say AI is becoming a mainstream technology in their company.

In the last few years, significant advances in open-source AI, such as the groundbreaking TensorFlow framework, have opened AI up to a broad audience and made the technology more accessible. Relatively frictionless use of the new technology has led to greatly accelerated adoption and an explosion of new applications. Tesla Autopilot, Amazon Alexa and other familiar use cases have both captured our imaginations and stirred controversy, but AI is finding applications in almost every aspect of our world.

Historically, machine learning (ML) the pathway to AI was reserved for academics and specialists with the necessary mathematical skills to develop complex algorithms and models. Today, the data scientists working on these projects need both the necessary knowledge and the right tools to be able to effectively productize their machine learning models for consumption at scale which can often be a hugely complicated task involving sophisticated infrastructure and multiple steps in ML workflows.

Another key piece is model lifecycle management (MLM), which manages the complex AI pipeline and helps ensure results. The proprietary enterprise MLM systems of the past were expensive, however, and yet often lagged far behind the latest technological advances in AI.

Effectively filling that operational capability gap is critical to the long-term success of AI programs because training models that give good predictions is just a small part of the overall challenge. Building ML systems that bring value to an organization is more than this. Rather than the ship-and-forget pattern typical of traditional software, an effective strategy requires regular iteration cycles with continuous monitoring, care and improvement.

Enter MLops (machine learning operations), which enables data scientists, engineering and IT operations teams to work together collaboratively to deploy ML models into production, manage them at scale and continuously monitor their performance.

MLops typically aims to address six key challenges around taking AI applications into production. These are: repeatability, availability, maintainability, quality, scalability and consistency.

Further, MLops can help simplify AI consumption so that applications can make use of machine learning models for inference (i.e., to make predictions based on data) in a scalable, maintainable manner. This capability is, after all, the primary value that AI initiatives are supposed to deliver. To dive deeper:

Repeatability is the process thatensuresthe ML modelwillrun successfully in a repeatable manner.

Availability means the ML model is deployed in a way that it is sufficiently available to be able to provide inference services to consuming applications and offer an appropriate level of service.

Maintainabilityrefers tothe processes thatenablethe ML modelto remainmaintainable on a long-term basis; for example, when retraining the model becomes necessary.

Quality: the ML model is continuously monitored to ensure it delivers predictions of tolerable quality.

Scalability means both the scalability of inference services and of the people and processes that are required to retrain the ML model when required.

Consistency: A consistent approach to ML is essential to ensuring success on the other noted measures above.

We can think of MLops as a natural extension of agile devops applied to AI and ML. Typically MLops covers the major aspects of the machine learning lifecycle data preprocessing (ingesting, analyzing and preparing data and making sure that the data is suitably aligned for the model to be trained on), model development, model training and validation, and finally, deployment.

The following six proven MLops techniques can measurably improve the efficacy of AI initiatives, in terms of time to market, outcomes and long-term sustainability.

ML pipelines typically consist of multiple steps, often orchestrated in a directed acyclic graph (DAG) that coordinates the flow of training data as well as the generation and delivery of trained ML models.

The steps within an ML pipeline can be complex. For instance, a step for fetching data in itself may require multiple subtasks to gather datasets, perform checks and execute transformations. For example data may need to be extracted from a variety of source systems perhaps data marts in a corporate data warehouse, web scraping, geospatial stores and APIs. The extracted data may then need to undergo quality and integrity checks using sampling techniques and might need to be adapted in various ways like dropping data points that are not required, aggregations such as summarizing or windowing of other data points, and so on.

Transforming the data into a format that can be used to train the machine learning ML model a process called feature engineering may benefit from additional alignment steps.

Training and testing models often require a grid search to find optimal hyperparameters, where multiple experiments are conducted in parallel until the best set of hyperparameters is identified.

Storing models requires an effective approach to versioning and a way to capture associated metadata and metrics about the model.

MLops platforms like Kubeflow, an open-source machine learning toolkit that runs on Kubernetes, translate the complex steps that compose a data science workflow into jobs that run inside Docker containers on Kubernetes, providing a cloud-native, yet platform-agnostic, interface for the component steps of ML pipelines.

Once the appropriate trained and validated model has been selected, the model needs to be deployed to a production environment where live data is available in order to produce predictions.

And theres good news here the model-as-a-service architecture has made this aspect of ML significantly easier. This approach separates the application from the model through an API, further simplifying processes such as model versioning, redeployment and reuse.

A number of open-source technologies are available that can wrap an ML model and expose inference APIs; for example, KServe and Seldon Core, which are open-source platforms for deploying ML models on Kubernetes.

Its crucial to be able to retrain and redeploy ML models in an automated fashion when significant model drift is detected.

Within the cloud-native world, KNative offers a powerful open-source platform for building serverless applications and can be used to trigger MLops pipelines running on Kubeflow or another open-source job scheduler, such as Apache Airflow.

With solutions like Seldon Core, it can be useful to create an ML deployment with two predictors e.g., allocating 90% of the traffic to the existing (champion) predictor and 10% to the new (challenger) predictor. The MLops team can then (ideally automatically) observe the quality of the predictions. Once proven, the deployment can be updated to move all traffic over to the new predictor. If, on the other hand, the new predictor is seen to perform worse than the existing predictor, 100% of the traffic can be moved back to the old predictor instead.

When production data changes over time, model performance can veer off from the baseline because of substantial variations in the new data versus the data used in training and validating the model. This can significantly harm prediction quality.

Drift detectors like Seldon Alibi Detect can be used to automatically assess model performance over time and trigger a model retrain process and automatic redeployment.

These are databases optimized for ML. Feature stores allow data scientists and data engineers to reuse and collaborate on datasets that have been prepared for machine learning so-called features. Preparing features can be a lot of work, and by sharing access to prepared feature datasets within data science teams, time to market can be greatly accelerated, whilst improving overall machine learning model quality and consistency. FEAST is one such open-source feature store that describes itself as the fastest path to operationalizing analytic data for model training and online inference.

By embracing the MLops paradigm for their data lab and approaching AI with the six sustainability measures in mind repeatability, availability, maintainability, quality, scalability and consistency organizations and departments can measurably improve data team productivity, AI project long-term success and continue to effectively retain their competitive edge.

Rob Gibbon is product manager for data platform and MLops at Canonical the publishers of Ubuntu.

Welcome to the VentureBeat community!

DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation.

If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers.

You might even considercontributing an articleof your own!

Read More From DataDecisionMakers

More here:
6 sustainability measures of MLops and how to address them - VentureBeat