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Golden Boy Promotions, one of boxing's most active and respected promoters, was founded in by world boxing champion Oscar De La Hoya. Working with APN Partner technology from Veritone and using Amazon S3, Rekognition, and Transcribe, Golden Boy can now share material reliably with broadcast partners for re-air and shoulder programming for promotional pieces, with the scale to auto-tag and more easily sort content and change layouts specific to each fight event.

Guinness Six Nations, an annual international six-week rugby tournament, uses AI and Machine Learning technology and predictive analytics to provide fans with new insights into the game. By measuring speed, cleanouts, steals, infringements, and time spent in the ruck Guinness Six Nations will provide a full ruck analysis.

And through the analysis of pack weights, player experience, and historical data Guinness Six Nations can predict the success of a scrum—raising the bar for what rugby fans around the world expect from their sports broadcasters. Cameras in all 29 NBA arenas collect and store 3D spatial data including ball and player locations and movements, and then use ML and advanced analytics to generate graphic overlays ranging from live layouts of basketball plays to frame-by-frame shot probabilities, giving fans a deeper understanding and appreciation for what happens on the court.

NASCAR, the largest stock car racing organization in the US, migrated its petabyte video archive containing 70 years of historical footage to AWS, and can use Amazon Rekognition to auto-tag video frames with metadata, such as driver, car, race, lap, time, and sponsors, and Amazon Transcribe to caption and time stamp every word within speech.

Real-time data is combined with traditional box score data to calculate hundreds of metrics and output predictions and insights delivered through graphics to fans online, onscreen, in stadium, and in second screen experiences, and as APIs to teams, analytics companies, broadcasters, and commentators.

PFF will use AWS compute, storage, database, serverless, analytics and machine learning services to improve operational efficiency, innovate at a faster pace and drive deeper meaning from game statistical data. AWS will enable PFF to uncover never-before-seen metrics that will change the way that teams, fans, and the media experience football. In addition, UIUC coaches now have auto-generated visual game planning sheets based on key features recommended by the ML model.

Second Spectrum uses AWS to help sports leagues and their media partners deliver analytics, visualizations, and easily searchable video clips to enhance the fan experience and provide teams with deeper competitive insights. Second Spectrum uses AWS Elemental to minimize latency in its streaming services while offering fans a high-quality viewing experience, and AWS Elemental MediaStore, a storage service optimized for media, to allow television producers and sports commentators to quickly access key moments from sports events.

Working with AWS, the team has used machine learning models to optimize relay formations, to give the best chance of winning medals at key competitions. AWS is proud to play a role in engaging, supporting and developing the next generation of recreational and elite Australian swimmers. For Sportradar, the global provider of sports and intelligence for the betting and media industries providing data coverage from more than , events annually, advances in computer vision are an opportunity to expand the depth of sports data offered to customers and reduce the costs of data collection through automation.

Sportradar is investing in computer vision research both through internal development and external partnerships to build computer vision data collection capabilities with an initial focus on tennis, soccer and snooker. Working with the Amazon ML Solutions Lab, Sportradar is exploring the application of state-of-the-art deep learning models for automated match event detection in soccer, moving beyond player and ball localisation to understanding the intent of the play in terms of what is happening in the game.

This infrastructure allows Sportradar's researchers to test and validate computer vision models at scale and bring models from the lab to production with minimal effort while delivering the low latency, reliability and scalability needed for live sports betting use cases.

To ensure fast disk performance for the thousands of simulations completed each week, the team also used Amazon FSx for Lustre to provide a fast, scalable, and secure high-performance file system based on Amazon Simple Storage Service Amazon S3. Pulselive is digital partner to some of the biggest sports leagues and events, from the Premier League to the Cricket World Cup. The Sony subsidiary is helping create fan experiences that increase engagement through new and impactful ways.

Pulselive uses Amazon Personalize to enable its customers to create highly personalized recommendations for their online sports media content. By tapping into the breadth and depth of AWS services, the NHL will be able to automate video processing and content delivery in the cloud and leverage its Puck and Player Tracking PPT System, which runs on AWS cloud infrastructure, to better capture the details of game play for its fans, teams, and media partners.

Powered by AWS, the system will encode, process, store, and transmit game footage from a series of new camera angles to provide continuous video feeds that capture plays and events outside the field of view of traditional broadcast cameras. The team surpassed our expectations, developing a 3D pose estimation pipeline using 2D videos captured with mobile phones in just two weeks.

The Pac Conference is dedicated to developing the next generation of leaders by championing excellence in academics, athletics and the care provided to its student-athletes. Pac engineers spin up new services rapidly and cost-effectively using AWS Lambda and Amazon EC2 , enabling functionality ranging from cloud-based production to data analytics, in support of the ultimate objective of creating amazing experiences for fans of Pac and college athletics everywhere.

Analogue data collected and analyzed by hand meant that live commentators provided color, and not much else. Now, fans, on-air talent, and teams benefit from automated data collection through sensors and cameras, and high performance computing means insights and analysis built using machine learning models—like the impact of an F1 car choosing to pit—can be processed and delivered in near real-time. Automating time-consuming, unreliable, and mundane tasks frees up valuable resources.

From ML models that forecast ticket sales or predicting the probability of a catch, to NASCAR using AI services to automatically tag media by detecting objects and translating speech to text, AI is allowing humans to do what they do best—more creative and strategic work. Coaches and teams forced to constantly react and change strategy, or to anticipate a play or action based on pure intuition, now have access to a treasure trove of predictive data during a match, game, or race, giving them the ability to make proactive and informed real-time decisions during a game—like an NFL coach pulling a player right before they run out of steam.

Any sports fan will tell you that their sport is as much a mental game as it is physical. By giving fans access to data and insights through visually rich on-screen graphics and interactive second screen experiences, sports organizations and broadcasters can peel back the curtain on the nuance of decision making, enriching fan experiences both inside and outside the stadium—like Six Nations Rugby showing real-time insights during a ruck.

The Amazon ML Solutions Lab pairs your team with our experts to build new machine learning solutions for your business. AWS machine learning partners offer a range of consulting services and technologies to help you explore and build the right solutions. Unlocking Data's Potential. The statement feeds expectations that the Democratic Congressional majority will pass — and that President Biden will sign — a bill to legalize marijuana.

Investors are also looking at further state-level legalization moves; one key state in this regard is New York. So, the cannabis industry is looking up. There is an expanding network of state legalization regimes, and expectations of a change in federal policy; both are putting upward pressure on cannabis shares.

Both have posted impressive year-to-date performances, and stand to rise even more in the year ahead. The company started out as a farmer, producing high-quality greenhouse vegetables year-round for sale in the North American market. That background fit the company well for a transition to the cannabis industry — Village Farms has experience in greenhouse production and industrial-scale growing. Two important pieces of news precipitated the surge since the end of January.

The move increases the international reach of Village Farms, and its ability to increase Altum holdings in the future. The company was able to fund these moves because it had a successful equity sale in January, putting an additional In addition to its strong capital and expansion positions, Village Farms has been reporting solid financial results.

VFF has historically been undervalued compared to less profitable peers, but we expect shares to continue working higher … as the prospect for US reform increases throughout the year. The company is involved in both the medical and recreational sides of the market, and both grows and produces cannabis and markets a range of products through numerous brand names.

Growth has been fueled by expansion of the cultivation operations in California and Pennsylvania, and by the move into the adult-use recreational market in New Jersey. Last month, TerrAscend closed a non-brokered private placement stock sale, putting more than 18 million common shares on the market. We have been bullish on the company since initiating coverage last year and are happy to say the TRSSF team has exceeded our expectations, generating rapid increases in margins and operating leverage that have earned them a place solidly in the Top Tier of MSOs," Des Lauriers noted.

Disclaimer: The opinions expressed in this article are solely those of the featured analysts. The content is intended to be used for informational purposes only. It is very important to do your own analysis before making any investment. For all the attention given to the argument that the stock market is in a bubble, it is important to point out that not everyone shares that view. In a monthly webinar, Wood made the argument against stocks being in a bubble.

Bloomberg -- Apple Inc. The secret project has gained momentum in recent months, adding multiple former Tesla Inc. The initiative, known as Project Titan inside Apple, is attracting intense interest because of its potential to upend the automotive industry and supply chains, much like the iPhone did to the smartphone market.

The following companies -- whose representatives declined to comment -- are possible candidates:FoxconnFoxconn Technology Group already has a close relationship with Apple. For well over a decade, it has been the U. It also plans to release a solid-state battery by MagnaMagna, based in Ontario, Canada, is the third-largest auto supplier in the world by sales, and has a contract-manufacturing operation with years of experience making entire car models for a variety of auto brands.

Magna produces everything from chassis and car seats to sensors and software for driver-assistance features. Magna also pitches its engineering and manufacturing services to EV startups. Last fall, it agreed to provide Fisker Inc. Hyundai or KiaHyundai Motor Co.

Hyundai and Kia both have plants in the U. While the two sell EVs derived from existing models, they will start selling vehicles based on the dedicated EV platform from March, helping to bring down costs and improve performance efficiency. They plan to introduce a combined 23 new EV models and sell 1 million units globally by The big disadvantage Hyundai and Kia have is the recent back-and-forth on whether they are developing a car for Apple, a notoriously secretive company.

After pursuing a strategy of volume at any cost that ate into profit, Nissan needs to attract higher-paying customers largely with the technology inside of its cars. StellantisOne factor in determining the suitability of a partner for Apple may be availability of production capacity. Stellantis is under pressure to find synergies after forming last month through the merger of PSA Group and Fiat Chrysler.

Retirement account owners have long had trouble translating the money in their k into income. Sun founded blockchain business Tron in and has since expanded into other decentralization technologies and platforms such as BitTorrent Inc. Sun said he advised Buffett to buy Bitcoin and Tesla when he dined with him last year. DLive, the live-streaming platform that was bought by BitTorrent late last year, has also come under scrutiny from American lawmakers for its role in broadcasting the U.

Capitol riots. Sun declined to comment on the controversies. Buying a stock is easy, but purchasing the right stock without a proven strategy is incredibly hard. Here are the best Robinhood stocks to buy now. The Apple Inc. The South Korean company - after the first successful approaches last January - was ready to make the Kia plant in West Point Georgia available to Apple, but some days ago the process came to a screeching halt, apparently due to internal disagreements within the Hyundai board.

Apple's goal would be to strike an agreement with an Asian company, probably to intercept the potential endless electric car market in the continent. See Also: Why Apple Could Emerge As Tesla's 'First True Competitor' Time Until "We are receiving several requests for cooperation in the joint development of autonomous electric vehicles from various companies, but they are at an early stage and nothing has been decided," Hyundai executives said in a note in which they dismissed the deal with Apple.

In conclusion, the Apple Car will have to wait for now: there is time until , which is the expected release date.

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Step 1: Specify the aim of your. Step 2: Select the metric. Step 3: Collect, group and modify data.