A new wave of change in how consumers interact with services is on the horizon, driven by the growing demand for rapid responses from customers. According to Okwechime, AI-powered Chatbots leverage Natural Language Processing to understand conversations and their contexts to such an advanced level that appropriate responses can be generated. He explained that recent advances have gone beyond text-based chats, with companies like Apple (Siri), Google, and Amazon (Alexa), introducing voice-controlled virtual assistants, able to answer an infinite number of questions proficiently in natural language dialogue. "In a market like Africa where literacy rate falls heavily behind the world's average, voice-controlled products and services will revolutionise how a consumer interacts with technology platforms, reducing entry barriers and improving technology adoption," he said.

Why, I wondered, was the enthusiasm so high at this week's Techonomy Climate conference in Mountain View? So I asked a smart friend why climate action suddenly commands so much passion. "The pandemic helped people realize a disaster can strike everyone on the planet all at once," they answered. "Almost none of us really thought it was possible before." It was as good an explanation as any.

John Quackenbush was frustrated with Google. It was January 2020, and a team led by researchers from Google Health had just published a study in Nature about an artificial intelligence (AI) system they had developed to analyze mammograms for signs of breast cancer. The system didn't just work, according to the study, it worked exceptionally well. When the team fed it two large sets of images to analyze--one from the UK and one from the U.S.--it reduced false positives by 1.2 and 5.7 percent and false negatives by 2.7 and 9.4 percent compared with the original determinations made by medical professionals. In a separate test that pitted the AI system against six board-certified radiologists in analyzing nearly 500 mammograms, the algorithm outperformed each of the specialists. The authors concluded that the system was "capable of surpassing human experts in breast cancer prediction" and ready for clinical trials. An avalanche of buzzy headlines soon followed. "Google AI system can beat doctors at detecting breast cancer," a CNN story declared.

We are trying to use the data that is recorded on the wind turbines to predict failures," Kalyan Veeramachaneni, principal research scientist in the Laboratory for Information and Decision Systems of the Massachusetts Institute of Technology, told DW. Ewald Hesse, CEO of Berlin-based Grid Singularity, says several countries in Africa would leapfrog the development phase of European energy systems, similar to what happened to landline phones. "In developing countries, there is no stringent regulation in the energy sector, and we don't need to convince the government of allowing a new approach to energy production and consumption. Still, local communities would benefit from one PV system in the surrounding area, which, combined with sensors to measure energy consumption, would create a localized market. "Whatever comes out in the energy field in developing countries will be by far smarter and more practical than what we have in Germany," said Hesse, adding that several companies contributed to unlocking potential markets and significant investments in developing countries.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - August 3. Join AI and data leaders for insightful talks and exciting networking opportunities. By this point, it's become reflexive: When searching for something on Google, Wikipedia is the de facto go-to first page. The website is consistently among the top 10 most-visited websites in the world. Yet, not all changemakers and historical figures are equally represented on the dominant web encyclopedia. Just 20% of Wikipedia biographies are about women.

Using algorithms and artificial intelligence, a research team led by Universidad Complutense de Madrid (UCM) has designed a tool which, in its initial trials, proved capable of predicting those areas with best access to potable groundwater in Africa, with a success rate of close to 90%. In specific terms, the papers published in Hydrology and Earth System Science and Geocarto International describe the hydrogeological mapping performed by the MLMapper software in the regions of Bamako and Koulikoro (Mali) and the region of Ouaddaï (Chad), respectively. "Ensure access to water and sanitation for all" is Sustainable Development Goal 6. In sub-Saharan Africa, groundwater plays a fundamental role in the supply of drinking water, but the percentage of wells that strike water is very often lower than 30%. "This is mainly because of a lack of hydrogeological knowledge, with the practical consequence that millions of euros of humanitarian aid are lost in fruitless drilling operations", underlines Víctor Gómez-Escalonilla Canales, a researcher at UCM's Department of Geodynamics, Stratigraphy and Palaeontology.

In December 2021 and January 2022, we asked recipients of our Data and AI Newsletters to participate in our annual survey on AI adoption. We were particularly interested in what, if anything, has changed since last year. Are companies farther along in AI adoption? Do they have working applications in production? Are they using tools like AutoML to generate models, and other tools to streamline AI deployment? We also wanted to get a sense of where AI is headed. The hype has clearly moved on to blockchains and NFTs. AI is in the news often enough, but the steady drumbeat of new advances and techniques has gotten a lot quieter. Compared to last year, significantly fewer people responded. This year's survey ran during the holiday season (December 8, 2021, to January 19, 2022, though we received very few responses in the new year); last year's ran from January 27, 2021, to February 12, 2021. Pandemic or not, holiday schedules no doubt limited the number of respondents.

The COVID-19 pandemic has devastated many low- and middle-income countries, causing widespread food insecurity and a sharp decline in living standards1. In response to this crisis, governments and humanitarian organizations worldwide have distributed social assistance to more than 1.5 billion people2. Targeting is a central challenge in administering these programmes: it remains a difficult task to rapidly identify those with the greatest need given available data3,4. Here we show that data from mobile phone networks can improve the targeting of humanitarian assistance. Our approach uses traditional survey data to train machine-learning algorithms to recognize patterns of poverty in mobile phone data; the trained algorithms can then prioritize aid to the poorest mobile subscribers. We evaluate this approach by studying a flagship emergency cash transfer program in Togo, which used these algorithms to disburse millions of US dollars worth of COVID-19 relief aid. Our analysis compares outcomes—including exclusion errors, total social welfare and measures of fairness—under different targeting regimes. Relative to the geographic targeting options considered by the Government of Togo, the machine-learning approach reduces errors of exclusion by 4–21%. Relative to methods requiring a comprehensive social registry (a hypothetical exercise; no such registry exists in Togo), the machine-learning approach increases exclusion errors by 9–35%. These results highlight the potential for new data sources to complement traditional methods for targeting humanitarian assistance, particularly in crisis settings in which traditional data are missing or out of date. Machine-learning algorithms can take advantage of survey and mobile phone data to help to identify people most in need of aid, complementing traditional methods for targeting humanitarian assistance.

Manipulation of deformable objects is a challenging task for a robot. It will be problematic to use a single sensory input to track the behaviour of such objects: vision can be subjected to occlusions, whereas tactile inputs cannot capture the global information that is useful for the task. In this paper, we study the problem of using vision and tactile inputs together to complete the task of following deformable linear objects, for the first time. We create a Reinforcement Learning agent using different sensing modalities and investigate how its behaviour can be boosted using visual-tactile fusion, compared to using a single sensing modality. To this end, we developed a benchmark in simulation for manipulating the deformable linear objects using multimodal sensing inputs. The policy of the agent uses distilled information, e.g., the pose of the object in both visual and tactile perspectives, instead of the raw sensing signals, so that it can be directly transferred to real environments. In this way, we disentangle the perception system and the learned control policy. Our extensive experiments show that the use of both vision and tactile inputs, together with proprioception, allows the agent to complete the task in up to 92% of cases, compared to 77% when only one of the signals is given. Our results can provide valuable insights for the future design of tactile sensors and for deformable objects manipulation.

Mood is a unique way for researchers to try to measure the impact of natural or unnatural disasters on people. However, it's simply impractical to ask every single person in the world how they're feeling in the aftermath of a sweeping event. But scientists from the Massachusetts Institute of Technology, the Chinese Academy of Sciences, and the Max Planck Institute for Human Development found a workaround. They used machine learning techniques to scan social media for sentiment shifts following the first wave of COVID-19 in 100 different countries and get real-time reads on how happy or sad the events related to the pandemic made people across the world. Think of the process as an AI-powered mood ring, but for millions of people.