Ever since the concept of Artificial Intelligence emerged, it became one of the most talked-about trends in the world. People see AI as "the new normal" as it has made its way into different work processes in all most all kinds of industries from augmented analytics to facial surveillance. Throughout 2018, we saw an incredible surge in platforms, tools, and applications focused on AI-and Machine Learning technologies. Ai and ML started in the internet and software trade, but now, we can also see them in different aspects of manufacturing, agriculture, healthcare, and more. Get the entire 10-part series on our in-depth study on activist investing in PDF. Save it to your desktop, read it on your tablet, or print it out to read anywhere!

Just over two years ago, China announced an audacious plan to overtake the US and lead the "world in AI [artificial intelligence] technology and applications by 2030". It is already widely regarded to have overtaken the EU in many aspects. But now its plans may be knocked off course by the US restricting certain Chinese companies from buying technologies developed or manufactured in the States. Washington's justification is that the organisations involved have made products used to commit human rights abuses against China's Muslim ethnic minorities. But it is notable that those on its blacklist include many of China's official "national AI champions", among them: Like the telecoms firm Huawei before them, they now face major disruption as a consequence of the Trump administration's intervention.

Tree ensemble methods such as gradient boosted decision trees and random forests are among the most popular and effective machine learning tools available when working with structured data. Tree ensemble methods are fast to train, work well without a lot of tuning, and do not require large datasets to train on. In TensorFlow, gradient boosted trees are available using the tf.estimator API, which also supports deep neural networks, wide-and-deep models, and more. For boosted trees, regression with pre-defined mean squared error loss (BoostedTreesRegressor) and classification with cross entropy loss (BoostedTreesClassifier) are supported.

The journal continues to take on big and tough issues as exemplified by the November 2018 special issue "Machine Learning in Health and Biomedicine." As computational power increases exponentially, the capacity to (more affordably) handle, store, and analyze "big data" using machine learning (ML) will revolutionize science and medicine. The power of ML is to find patterns among variables in large data sets rather than being programmed with rules. Models become more complex when they move from supervised (input and outputs have labels) to unsupervised (no labels), and when they move from linear regression with decision trees to neural networks ( 3 neural networks is termed deep learning). As the complexity increases so does one's ability to "interpret" the data.

Today, there's a common notion that artificial intelligence (AI) is going to put us all out of jobs. But recently, I read an article (subscription required) where AI expert Robert Atkinson remarked that worries around AI and job loss are overblown. He said, "It's time to take a deep breath and stop panicking about artificial intelligence and what it portends for jobs. No, AI won't destroy more jobs than it creates. No, the pace of technological change is not accelerating. And no, we certainly don't need to tax AI to slow it down."

Diligent Robotics, maker of an autonomous robot assistant for hospitals, has raised $3 million in seed funding. The raise was headed by True Ventures and Ubiquity Ventures, as well as Next Coast Ventures, Capital Factory, Pathbreaker Ventures, Boom Capital, Grit Ventures and other unnamed angel investors. This adds to another $2.1 million round announced back in the beginning of 2018, as well as a $725,000 National Science Foundation grant. This week's funding news was complemented with an announcement that the startup's hospital robot, called Moxi, is exiting beta testing and has entered market with its first official rollout in a Texas Hospital. While Diligent Robotics' broad aim is to develop robotic assistants for a range of chores or activities, the company has so far focused on healthcare use cases for its technology with Moxi.

I. Is AI doing any good at all? Researchers, entrepreneurs, and policy-makers are increasingly using AI to tackle development challenges. In other words, using AI for a greater good is a real thing. However, it is becoming clear that AI poses as many threats as benefits, although the former ones are usually neglected. I do not want to get into trust, accountability, or safety issues in this short piece (if you want, here there is more), but avoiding the negative effects of AI is why incorporating a set of ethical principles into our technology development process is so paramount. Ethics plays a key role by ensuring that regulations of AI harness its potential while mitigating its risks (Taddeo and Floridi, 2018) and it would help us understand how to use responsibly the power coming from this technology.

While touching many facets of our daily lives, this advanced technology AI now walks hand in hand with us. With each passing day, we are relying more on it! Our usage of AI-based voice assistants like Siri, Alexa, and Cortana are splendid examples. As time passes more in the future, these VA's will increase, and we will become more accustomed to them! The same is happening in the eCommerce industry, where AI is reshaping it!

Novartis and Microsoft plan to use artificial intelligence to develop new drugs faster, and with greater precision. Novartis and Microsoft have agreed to a five-year partnership to use artificial intelligence (AI) to develop new drugs faster, and with greater precision. Novartis CEO Vas Narasimhan said AI's potential for personalized medicine is particularly promising, as it will help classify subgroups of patients that new drugs will most likely benefit, using clinical and preclinical datasets. Narasimhan added that these datasets were consolidated from Novartis' internal information, surmounting integration obstacles. The ultimate goal of the alliance is to use AI to fuel research, development, manufacturing, finance, sales, marketing, and acquisitions.

Launched in January this year, the Maha Agri Tech project seeks to use technology to address various cultivation risks ranging from poor rains to pest attacks, accurately predict crop-wise and area-wise yield and eventually to use this data to inform policy decisions including pricing, warehousing and crop insurance. When farmers in six districts of Maharashtra begin sowing for the coming rabi season, this project will enter its second phase where artificial intelligence and satellite imagery will be used to mitigate risks. Fields of the farmers that are part of the project will be monitored via satellite images at every stage right until the harvest. In its first phase the Maha Agri Tech project used satellite images and analysis from the Maharashtra Remote Sensing Application Centre (MRSAC) and the National Remote Sensing Centre (NRSC) in Hyderabad to assess the acreage and the conditions of select crops in select talukas. In its second phase, various data sets from diverse data providers will be combined to build yield modelling and a geospatial database of soil nutrients, rainfall, moisture stress and other parameters to facilitate location-specific advisories to farmers.