Machine learning to help predict future dengue fever outbreaks – The Jakarta Post


Recently, machine learning, a subset of artificial intelligence, has been used to make predictions about where the next outbreak will occur.

This AI tool lets you visualize how climate change could affect your home

USATODAY - Tech Top Stories

A new tool with cutting-edge image recognition AI lets you visualize the future effects of climate change on any place in the world -- including your own home. The project, titled "This Climate Does Not Exist," lets you enter the address of your current home or your favorite travel destination and see what it could look like years later once climate change has taken its toll. You can see how Disneyland will look like covered in smog, the way extreme smog blanketed Beijing in 2014. You can see what your childhood home will look like after it is flooded by rising sea levels, the way floods devastated Indonesia in 2020 after widespread deforestation. Jakarata floods: Thousands caught in floods in Jakarta, Indonesia's sinking capital Extreme weather events due to climate change are already impacting corners of the globe.

Senior Data Scientist - Demand Generation


About the Role As our Senior Data Scientist, you'll be an integral player in the Demand and Incentives Data Team based in Jakarta. With the latest cutting-edge data science tech at your disposal, you'll focus your efforts on bringing our incentive systems to the next level, employing various quantitative techniques such as Machine Learning, Optimization, Simulation, Bayesian Techniques to drive asymmetric values for our businesses at Gojek. You'll be heavily involved in ideation, research, and building prototypes, and the folks in the Data Science Platform will bring your models to production. Your efforts will directly influence the stability and scalability of Gojek's demand & incentives stream, and thus to company's top and bottom line as a whole. What You Will Do Drive the long term vision of the Machine Learning-based incentive systems and own its implementation end-to-end Enhance the technical excellence of the team and bring the data science products in your stream to the next level Work with other Data Scientists, Machine Learning Engineers, and Business users to build, deploy, and scale data science solutions for incentive systems Utilize your experience in data science, machine learning, software engineering, distributed systems to develop these systems; work with the platform team to take the systems to production Work with Business teams to continuously refine and improve the systems to cater to Gojek's ever-evolving needs What You Will Need At least 5 years of experience as a Data Scientist/ Machine Learning Engineer, with solid understanding of Data Science and Machine Learning fundamentals and experience taking Data Science models into production Experience in Python, R, Golang/Java, Unix; along with knowledge of good software design principles and TDD Working knowledge of Cloud-based solutions (GCP/ AWS), Stream Data Processing Frameworks (Beam) and mature Deep Learning frameworks (e.g.

Machine Learning Engineer - Merchant Platform


About the Role Fasten your helmet and climb on board if you're ready to be our Machine Learning Engineer. In this role, you'll be a crucial player within the Merchant Platform, using and building machine learning as a microservice, integrating it with the core service, and establishing data pipelines for structured & unstructured data. In close collaboration with the Data Science, Data Engineering, and Product Engineering teams, you'll get your hands dirty in complex ML, data pipeline, and service product tech stacks. By automating processes and integrating ML models into our products & services, your efforts will help ensure a robust and efficient Merchant Platform for Gojek. What You Will Do Collaborate with Data Science team to gather the requirement for model parameter Build the feature extraction script to automate the process for the ML model Collaborate with product engineer to integrate ML model into product/service Process data from streaming/raw data based on user needs Collaborate with other Data Engineers to develop data and model pipelines Design distributed systems to apply machine learning and data science techniques What You Will Need At least 2 years of experience as a Software Engineer or ML Engineer, with fluency and experience in Clojure, Elixir, Python, or Java Basic knowledge in data science, and familiarity with ML libraries such as Pandas, Scikit, or Tensorflow Proven track-record in building large-scale, high-throughput, low-latency production systems Experience building data stream processes Familiarity with SQL and NoSQL Database Ability to implement CI/CD and TDDAbout the Team Our Merchant Platform team is a big family of around 60 people based across Jakarta, Yogyakarta, and India.

Surge Pricing, Artificial Intelligence, and Responsibility


On my first work trip to Jakarta 14 January 2016 for Grab, multiple terrorist bombs exploded a couple of miles from the GrabBike office where I had just arrived. People were fleeing cafes and restaurants around the attack site. My new colleagues were shaken, glad to be safe, looking to help. There was news of crowds on the streets trying to get away, confirmed by a spike in booking requests from the blocks around the explosion. My colleagues remembered the 2002 Bali bombings, and knew we should get people to spread out.

Halodoc Harnesses AI To Push Telehealth Forward


An Indonesian company is helping to push telemedicine forward by using artificial intelligence (AI) to give doctors feedback on how to improve patient care, according to Google's The Keyword. Jakarta-based Halodoc says that its telehealth platform uses AI to provide doctors with the feedback and mentoring that they would receive from fellow doctors in an in-person setting like a hospital. Halodoc has been developing the product with machine learning experts from Google's Late-Stage Accelerator using natural language processing in Bahasa Indonesia. The machine learning models are trained using information from thousands of doctor consultations, according to the Google report. Halodoc's app allows doctors to receive feedback on how well and quickly they perform services, along with advice on how to improve their patient consultations and an option to receive additional coaching from fellow physicians, according to the company.

Google Maps Keep Getting Better, Thanks To DeepMind's Machine Learning


Google users contribute more than 20 million pieces of information on Maps every day – that's more than 200 contributions every second. The uncertainty of traffic can crash the algorithms predicting the best ETA. There is also a chance of new roads and buildings being built all the time. Though Google Maps gets its ETA right most of the time, there is still room for improvement. Researchers at Alphabet-owned DeepMind have partnered with the Google Maps team to improve the accuracy of the real-time ETAs by up to 50% in places like Berlin, Jakarta, São Paulo, Sydney, Tokyo, and Washington D.C.

Google Maps and DeepMind enhance AI capabilities to improve route calculations


It has been nearly 13 years since Google Maps started providing traffic data to help people navigate their way around, alongside providing detail about whether the traffic along the route is heavy or light, the estimated travel time, and the estimated time of arrival (ETAs). In a bid to further enhance those traffic prediction capabilities, Google and Alphabet's AI research lab DeepMind have improved real-time ETAs by up to 50% in places such as Sydney, Tokyo, Berlin, Jakarta, Sao Paulo, and Washington DC by using a machine learning technique known as graph neural networks. Google Maps product manager Johann Lau said Google Maps uses aggregate location data and historical traffic patterns to understand traffic conditions to determine current traffic estimates, but it previously did not account for what traffic may look like if a traffic jam were to occur while on the journey. "Our ETA predictions already have a very high accuracy bar -- in fact, we see that our predictions have been consistently accurate for over 97% of trips … this technique is what enables Google Maps to better predict whether or not you'll be affected by a slowdown that may not have even started yet," he said in a blog post. The researchers at DeepMind said by using graph neural networks, this allowed Google Maps to incorporate "relational learning biases to model the connectivity structure of real-world road networks."

Google Maps is improving travel ETAs with DeepMind AI


Google Maps helps users navigate over one billion kilometers in more than 200 countries and territories daily, and Google says its estimated time of arrival (ETA) predictions have been consistently accurate for over 97 percent of trips. That's not good enough for Google, though, so the company partnered with DeepMind to use machine learning to make its ETAs even more accurate. Before partnering with DeepMind, an Alphabet AI research lab, Google Maps used a combination of historical traffic patterns and live traffic conditions to understand current traffic patterns. The partners wanted to be able to predict future traffic patterns, so DeepMind developed a graphic neural network, which also considers data on the time of year, road quality, speed limits, accidents and closures. Thanks to that machine learning approach, Google Maps has improved the accuracy of real-time ETAs by up to 50 percent in places like Berlin, Jakarta, São Paulo, Sydney, Tokyo, and Washington D.C. Now, Google Maps can warn users about traffic jams before they exist.

Google Developers brings its Machine Learning Bootcamp to Indonesia


Last October, Google Developers brought their Machine Learning Bootcamp to Jakarta, Indonesia! ML Bootcamp is a one-stop solution to learn about Google's latest machine learning offerings from both Googlers and other industry experts. The 4-day intensive bootcamp consists of instructor-led trainings, hands-on codelabs, and saw 35 companies, as well as 12 startups represented from across Indonesia. If you're an aspiring ML developer, be sure to check out the following online courses: ML crash course with TensorFlow APIs