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On the side of a dusty Jakarta road, nestled in the corner of a gas station, might be the future of coffee. Driving home, you might preorder a cup as you plan to refill the car, even before pulling up to the gas station. Open the Kopi Kenangan app, click on "preorder," and choose one -- perhaps a mellow coffee with just a hint of acidity, creamy with milk. By the time you arrive at the station, the iced coffee, made by a human, will be sitting on the countertop, sweating in a plastic cup in the Jakarta humidity. Indonesian coffee chain Kopi Kenangan -- "coffee memories" in Bahasa Indonesia -- did not set out to be a tech-powered coffee chain.
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.
Charles is currently editorial director for Asia at Economist Impact. He covers a territory spanning from Australia to India. His team works with many Western multinationals from the Fortune 500 but increasingly with Asian multinationals, governments, SMEs and high-growth technology firms as well. A native Australian, Charles is currently based in Singapore and has most recently managed the regions technology research practice. He is a frequent speaker at technology events, recently giving keynote presentations at events in Singapore, Australia, Jakarta and Kuala Lumpur.
SINGAPORE – Five months since the military toppled a democratically elected government in Myanmar, ASEAN has not been able to appoint a special envoy to help defuse the member country's political crisis -- and a major obstacle seems to be disunity within the group. Disagreement among Association of Southeast Asian Nations countries over the envoy's selection appears to be helping Myanmar's military, which wants to buy time to solidify its rule, but that has also led to frustrations for some within ASEAN who want to engage in the issue more actively. Leaders of the 10-member group had agreed at a summit in the Indonesian capital Jakarta in April on a "five-point consensus" that included appointing a special ASEAN envoy to Myanmar. The envisaged envoy would try to mediate in the dialogue process between various parties in the country -- where pro-democracy forces were ousted from power in the Feb. 1 coup, with civilian leader Aung San Suu Kyi put under house arrest. ASEAN sources say there are three nominees: Virasakdi Futrakul, a former Thai deputy foreign minister and veteran diplomat; Hassan Wirajuda, a former Indonesian foreign minister, and Razali Ismail, a Malaysian who was a U.N. special envoy for Myanmar in the 2000s tasked with facilitating national reconciliation and democratization in the country.
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.
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.
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.
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 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.