Machine learning is becoming one of most rapidly growing technical fields, benefiting tremendous areas in science and industry. Recently, Breen et al demonstrated a new study with a multi-layered deep artificial neural network (ANN) on a chaotic three-body system and claimed that it can bring “success in accurately reproducing the results of a chaotic system” with “up to 100 million times faster than numerical integrator”. Here, we use their trained ANN model to predict periodic orbits of the same three-body system, but the detailed comparisons are disappointing. It might be due to the butterfly-effect of chaotic systems, i.e. very sensitive to tiny disturbance, but in practice nearly all ML algorithms derive their solutions statistically and probabilistically and therefore are rarely possible to train them to 100% accuracy. We illustrate here that the current accuracy of the machine learning is not precise enough for correct prediction of periodic orbits of a chaotic three-body system in a long enough duration. Thus, it is still a great challenge for machine learning to solve chaotic systems, such as the famous three-body problem. Without doubt, studies in machine learning on chaotic systems might greatly deepen and enrich our understandings not only on chaos but also on machine learning itself.