Cancer is the second leading cause of death worldwide[i], with an estimated 18.1 million new cases and 9.6 million deaths attributed to it in 2018[ii]. The search for more effective anti-cancer drugs is a global effort involving academia and industry. In our Computational Systems Biology group at the IBM Research lab in Zurich, we are building machine learning approaches that can potentially help to accelerate our understanding of the leading drivers and molecular mechanisms of these complex diseases, as well as the differences in tumor composition occurring across various cancer types. Our goal is to deepen our understanding of cancer to equip industries and academia with the knowledge that could potentially one day help fuel new treatments and therapies. At the 18th European Conference on Computational Biology (ECCB) and the 27th Conference on Intelligent Systems for Molecular Biology (ISMB) to be held from July 21 -25 in Basel, Switzerland, IBM will present significant, novel research that led to the implementation of three machine learning solutions aimed at accelerating and guiding cancer research.

One of the most important innovations in the present era for the development of highly-advanced AI systems has been the introduction of Reinforcement Learning (RL). It has the potential to solve complex decision-making problems. It generally follows a "trial and error" method to learn optimal policies of a given problem. It has been used to achieve superhuman performance in competitive strategy games, including Go, Starcraft, Dota, among others. Despite the promise shown by reinforcement algorithms in many decision-making problems, there are few glitches and challenges, which still need to be addressed.

Today, Mozilla and the German Ministry for Economic Cooperation and Development (BMZ) have announced to join forces in the collection of open speech data in local languages, as well as the development of local innovation ecosystems for voice-enabled products and technologies. The initiative builds on the pilot project, which our Open Innovation team and the Machine Learning Group started together with the organization "Digital Umuganda" earlier this year. The Rwandan start-up collects language data in Kinyarwanda, an African language spoken by over 12 million people. Further languages in Africa and Asia are going to be added. Mozilla's projects Common Voice and Deep Speech will be the heart of the joint initiative, which aims at collecting diverse voice data and opening up a common, public database.

Even if you don't have a chatbox on your website, you have definitely encountered one before. Facebook, eBay, Domino's Pizza, and Universal Studios are some of the big names that have their own chatbots. With the advancements in AI, mundane tasks like customer service can easily be handled by a chatbot. Some people reject the merit of chatbots stating that communicating with them feels very impersonal. According to the 2018 State of Chatbots Report by Salesforce, 69% of consumers said they preferred communicating with chatbots.

As per the report by International Data Corporation, almost 50 percent of the participating global organizations perceived Artificial Intelligence as a top priority. While 25 percent of these businesses had successfully implemented a company-wide AI strategy, almost 60 percent had modified their business model to accommodate AI-driven functionalities. However, one-fourth of the respondents reported that up to 50 percent of their AI projects couldn't provide the desired results. Overall, many businesses experience issues when trying to incorporate Artificial Intelligence (also known as AI) into their operations. There are various reasons for this, such as the higher costs involved, the requirement of specialist skills, and reliance on the collection of comprehensive data.

Growing number of cross-industry collaborations and partnerships and the need to control drug discovery & development costs and reduce the overall time taken in this process are the key factors driving the AI in the drug discovery market. The global AI in the drug discovery market is projected to reach USD 1,434 million by 2024 from USD 259 million in 2019, at a CAGR of 40.8% during the forecast period. Growth in this market is mainly driven by growing number of cross-industry collaborations and partnerships, the need to control drug discovery & development costs and reduce the overall time taken in this process, the rising adoption of cloud-based applications & services, and the impending patent expiry of blockbuster drugs. On the other hand, a lack of data sets in the field of drug discovery and the inadequate availability of skilled labor are some of the factors challenging the growth of the market. The immuno-oncology segment accounted for the largest share in 2019.

Massachusetts State Police (MSP) has been quietly testing ways to use the four-legged Boston Dynamics robot known as Spot, according to new documents obtained by the American Civil Liberties Union of Massachusetts. And while Spot isn't equipped with a weapon just yet, the documents provide a terrifying peek at our RoboCop future. The Spot robot, which was officially made available for lease to businesses last month, has been in use by MSP since at least April 2019 and has engaged in at least two police "incidents," though it's not clear what those incidents may have been. It's also not clear whether the robots were being operated by a human controller or how much autonomous action the robots are allowed. MSP did not respond to Gizmodo's emails on Monday morning.

Machine learning today not only recommends the things you can buy, or content you can watch, but it is doing wonders in other domains as well. This time it is on a mission to find out something untouched. There are different elements in music that trigger emotion in humans. And machine learning is trying to find out just that -- how music affects brain activity, physiological response, and human-reported behaviour. New research by scholars from the University of Southern California is trying to figure out the elements in a song that triggers different emotions in a human.

This week's episode focuses on the interface between physics and computing, with deep dives into how artificial intelligence (AI) is contributing to medical physics and how silicon could form the basis of a future quantum computer. First, we hear from Tami Freeman, Physics World's resident expert on medical physics, about a new positron emission tomography (PET) scanner that can image a patient's whole body much more quickly (or at higher resolutions) than is possible with current commercial scanners. We then stick with the medical theme to discuss three recent examples of how AI is being used in medicine: firstly to diagnose skin conditions (but, disturbingly, only if the patient's skin is white); secondly to help radiologists detect lung tumours in X-rays; and thirdly to develop better radiotherapy treatment plans. There are several ways of constructing the qubits, or quantum bits, that make up a quantum computer, and this week we hear from a trio of researchers – Fernando Gonzalez-Zalba, Alessandro Rossi and Tsung-Yeh Yang – who have been developing silicon-based qubits. Their work is part of a Europe-wide collaboration between universities, government laboratories and companies called MOS-Quito, and you can read more about it in their article for the Physics World Focus on Computing.

Agenda: 6:00 - 6:30 pm: Social Hour with Food, Drinks, Beer & Wine 6:30 - 6:35 pm: Introduction & Announcements 6:35 - 7:05 pm: Talk 1 Managing the Full Deployment Lifecycle of Models with the MLflow Model Registry (Databricks) 7:05 - 7:35 pm: Talk 2 MLflow on and inside Azure (Microsoft) 7:35 - 8:05 pm: Talk 3 TensorFlow(X) Data Validation: Better ML through better data (Google) 8:05 - 8:30 pm: Additional Networking Talk 1 - Title: Managing the Full Deployment Lifecycle of Models with the MLflow Model Registry Presenter: Mani Parkhe, Databricks Abstract: MLflow is an open-source platform to manage the machine learning lifecycle, including experiment tracking, reproducible runs, and model packaging. In this talk, we provide an overview of the latest component of MLflow, the Model Registry, which serves as a collaborative hub where teams can share, discuss, use, inspect, and track the lineage of models. Model Registry was introduced in MLflow 1.4 and is in Private Preview on Databricks With this addition, MLflow provides end-to-end management of the deployment lifecycle of models from experimentation to online testing and production, complete with approval and governance workflows. Bio: Mani Parkhe is an ML/AI Platform Engineer at Databricks, focusing on the customer and open-source platform initiatives, which enable data discovery, training, experimentation, and deployment of ML models on the cloud. After spending 15 years building software for semiconductor chip CAD, Mani transitioned to building big data infrastructure, distributed systems and web services, and machine learning platforms.